notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJS5eE8ScyjN"
      },
      "source": [
        "# **1. Library Import**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34BDNqOddFvx",
        "outputId": "3a632abb-d325-415c-c70e-969e21e9c8cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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 0:00:25\n",
            "🔁 Restarting kernel...\n",
            "conda 22.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()\n",
        "!conda --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h796lFG0dHUK",
        "outputId": "ade2dccf-9d75-4ac0-f266-4eb7a9470d09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 22.9.0\n",
            "  latest version: 22.11.1\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local/envs/stroke-detection\n",
            "\n",
            "  added / updated specs:\n",
            "    - python==3.9.1\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2022.12.7  |       ha878542_0         143 KB  conda-forge\n",
            "    libffi-3.3                 |       h58526e2_2          51 KB  conda-forge\n",
            "    openssl-1.1.1s             |       h0b41bf4_1         1.9 MB  conda-forge\n",
            "    python-3.9.1               |hffdb5ce_5_cpython        27.3 MB  conda-forge\n",
            "    setuptools-66.1.0          |     pyhd8ed1ab_0         628 KB  conda-forge\n",
            "    sqlite-3.40.0              |       h4ff8645_0         801 KB  conda-forge\n",
            "    tzdata-2022g               |       h191b570_0         106 KB  conda-forge\n",
            "    zlib-1.2.13                |       h166bdaf_4          92 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        31.0 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge None\n",
            "  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_gnu None\n",
            "  ca-certificates    conda-forge/linux-64::ca-certificates-2022.12.7-ha878542_0 None\n",
            "  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.39-hcc3a1bd_1 None\n",
            "  libffi             conda-forge/linux-64::libffi-3.3-h58526e2_2 None\n",
            "  libgcc-ng          conda-forge/linux-64::libgcc-ng-12.2.0-h65d4601_19 None\n",
            "  libgomp            conda-forge/linux-64::libgomp-12.2.0-h65d4601_19 None\n",
            "  libsqlite          conda-forge/linux-64::libsqlite-3.40.0-h753d276_0 None\n",
            "  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-12.2.0-h46fd767_19 None\n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.13-h166bdaf_4 None\n",
            "  ncurses            conda-forge/linux-64::ncurses-6.3-h27087fc_1 None\n",
            "  openssl            conda-forge/linux-64::openssl-1.1.1s-h0b41bf4_1 None\n",
            "  pip                conda-forge/noarch::pip-22.3.1-pyhd8ed1ab_0 None\n",
            "  python             conda-forge/linux-64::python-3.9.1-hffdb5ce_5_cpython None\n",
            "  readline           conda-forge/linux-64::readline-8.1.2-h0f457ee_0 None\n",
            "  setuptools         conda-forge/noarch::setuptools-66.1.0-pyhd8ed1ab_0 None\n",
            "  sqlite             conda-forge/linux-64::sqlite-3.40.0-h4ff8645_0 None\n",
            "  tk                 conda-forge/linux-64::tk-8.6.12-h27826a3_0 None\n",
            "  tzdata             conda-forge/noarch::tzdata-2022g-h191b570_0 None\n",
            "  wheel              conda-forge/noarch::wheel-0.38.4-pyhd8ed1ab_0 None\n",
            "  xz                 conda-forge/linux-64::xz-5.2.6-h166bdaf_0 None\n",
            "  zlib               conda-forge/linux-64::zlib-1.2.13-h166bdaf_4 None\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "setuptools-66.1.0    | 628 KB    | : 100% 1.0/1 [00:00<00:00,  2.65it/s]\n",
            "python-3.9.1         | 27.3 MB   | : 100% 1.0/1 [00:07<00:00,  7.14s/it]               \n",
            "ca-certificates-2022 | 143 KB    | : 100% 1.0/1 [00:00<00:00, 20.81it/s]\n",
            "libffi-3.3           | 51 KB     | : 100% 1.0/1 [00:00<00:00, 17.92it/s]\n",
            "openssl-1.1.1s       | 1.9 MB    | : 100% 1.0/1 [00:00<00:00,  8.04it/s]\n",
            "zlib-1.2.13          | 92 KB     | : 100% 1.0/1 [00:00<00:00, 15.53it/s]\n",
            "sqlite-3.40.0        | 801 KB    | : 100% 1.0/1 [00:00<00:00,  5.31it/s]\n",
            "tzdata-2022g         | 106 KB    | : 100% 1.0/1 [00:00<00:00,  5.48it/s]\n",
            "Preparing transaction: \\ \b\b| \b\b/ \b\bdone\n",
            "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "#\n",
            "# To activate this environment, use\n",
            "#\n",
            "#     $ conda activate stroke-detection\n",
            "#\n",
            "# To deactivate an active environment, use\n",
            "#\n",
            "#     $ conda deactivate\n",
            "\n",
            "Retrieving notices: ...working... done\n"
          ]
        }
      ],
      "source": [
        "!conda create --name stroke-detection python==3.9.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziUiyuupdI4z",
        "outputId": "4884fcab-2c41-434a-fa51-f88bf31fca93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.\n",
            "To initialize your shell, run\n",
            "\n",
            "    $ conda init <SHELL_NAME>\n",
            "\n",
            "Currently supported shells are:\n",
            "  - bash\n",
            "  - fish\n",
            "  - tcsh\n",
            "  - xonsh\n",
            "  - zsh\n",
            "  - powershell\n",
            "\n",
            "See 'conda init --help' for more information and options.\n",
            "\n",
            "IMPORTANT: You may need to close and restart your shell after running 'conda init'.\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!conda activate stroke-detection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bam0BC6IdKub",
        "outputId": "b021b83e-10bf-4973-e07d-0fd64b750b25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting jupyter\n",
            "  Downloading jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)\n",
            "Collecting scikit-learn\n",
            "  Downloading scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m42.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tensorflow\n",
            "  Downloading tensorflow-2.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (588.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m588.3/588.3 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tfx==1.11.0\n",
            "  Downloading tfx-1.11.0-py3-none-any.whl (2.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting flask\n",
            "  Downloading Flask-2.2.2-py3-none-any.whl (101 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.5/101.5 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Stored in directory: /root/.cache/pip/wheels/ca/cc/e3/f1e272f628fdb013d969acc99cfe2e031ea15b3efb74ffe842\n",
            "Successfully built google-apitools crcmod dill pyfarmhash docopt\n",
            "Installing collected packages: webencodings, wcwidth, tensorboard-plugin-wit, Send2Trash, pytz, pyfarmhash, pyasn1, ptyprocess, pickleshare, mistune, libclang, kt-legacy, keras, ipython-genutils, flatbuffers, fastjsonschema, docopt, crcmod, backcall, zipp, wrapt, websocket-client, webcolors, uritemplate, uri-template, typing-extensions, traitlets, tornado, tinycss2, threadpoolctl, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, sqlparse, soupsieve, sniffio, six, rsa, rfc3986-validator, regex, pyzmq, pyyaml, python-json-logger, pyrsistent, pyparsing, pymongo, pygments, pyasn1-modules, psutil, protobuf, prompt-toolkit, prometheus-client, platformdirs, pkgutil-resolve-name, pexpect, parso, pandocfilters, overrides, orjson, objsize, oauthlib, numpy, nest-asyncio, MarkupSafe, jupyterlab-widgets, jupyterlab-pygments, jsonpointer, joblib, itsdangerous, grpcio, google-crc32c, gast, fqdn, fasteners, fastavro, entrypoints, dill, defusedxml, decorator, debugpy, cloudpickle, click, cachetools, attrs, absl-py, Werkzeug, terminado, tensorflow-hub, scipy, rfc3339-validator, requests-oauthlib, python-dateutil, pydot, pyarrow, proto-plus, portpicker, packaging, opt-einsum, ml-metadata, matplotlib-inline, keras-preprocessing, jupyter-core, jinja2, jedi, importlib-resources, importlib-metadata, httplib2, hdfs, h5py, googleapis-common-protos, google-resumable-media, google-pasta, google-auth, docker, comm, bleach, beautifulsoup4, astunparse, argon2-cffi-bindings, anyio, tensorflow-metadata, scikit-learn, qtpy, pandas, oauth2client, markdown, kubernetes, jupyter-server-terminals, jupyter-client, jsonschema, ipython, grpcio-status, google-auth-oauthlib, google-auth-httplib2, google-api-core, flask, arrow, argon2-cffi, apache-beam, tensorboard, nbformat, isoduration, ipykernel, grpc-google-iam-v1, google-cloud-core, google-apitools, google-api-python-client, tensorflow, qtconsole, nbclient, ml-pipelines-sdk, keras-tuner, jupyter-console, google-cloud-vision, google-cloud-videointelligence, google-cloud-storage, google-cloud-spanner, google-cloud-resource-manager, google-cloud-recommendations-ai, google-cloud-pubsub, google-cloud-language, google-cloud-dlp, google-cloud-datastore, google-cloud-bigtable, google-cloud-bigquery-storage, google-cloud-bigquery, tensorflow-serving-api, nbconvert, jupyter-events, google-cloud-pubsublite, google-cloud-aiplatform, jupyter-server, tfx-bsl, notebook-shim, tensorflow-transform, tensorflow-data-validation, nbclassic, notebook, widgetsnbextension, ipywidgets, tensorflow-model-analysis, jupyter, tfx\n",
            "Successfully installed MarkupSafe-2.1.2 Send2Trash-1.8.0 Werkzeug-2.2.2 absl-py-1.4.0 anyio-3.6.2 apache-beam-2.44.0 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 arrow-1.2.3 astunparse-1.6.3 attrs-21.4.0 backcall-0.2.0 beautifulsoup4-4.11.1 bleach-5.0.1 cachetools-4.2.4 click-7.1.2 cloudpickle-2.2.1 comm-0.1.2 crcmod-1.7 debugpy-1.6.5 decorator-5.1.1 defusedxml-0.7.1 dill-0.3.1.1 docker-4.4.4 docopt-0.6.2 entrypoints-0.4 fastavro-1.7.0 fasteners-0.18 fastjsonschema-2.16.2 flask-2.0.3 flatbuffers-23.1.4 fqdn-1.5.1 gast-0.4.0 google-api-core-1.32.0 google-api-python-client-1.12.11 google-apitools-0.5.31 google-auth-1.35.0 google-auth-httplib2-0.1.0 google-auth-oauthlib-0.4.6 google-cloud-aiplatform-1.17.1 google-cloud-bigquery-2.34.4 google-cloud-bigquery-storage-2.13.2 google-cloud-bigtable-1.7.3 google-cloud-core-2.3.2 google-cloud-datastore-1.15.5 google-cloud-dlp-3.9.2 google-cloud-language-1.3.2 google-cloud-pubsub-2.13.11 google-cloud-pubsublite-1.6.0 google-cloud-recommendations-ai-0.7.1 google-cloud-resource-manager-1.6.3 google-cloud-spanner-3.26.0 google-cloud-storage-2.7.0 google-cloud-videointelligence-1.16.3 google-cloud-vision-3.1.4 google-crc32c-1.5.0 google-pasta-0.2.0 google-resumable-media-2.4.1 googleapis-common-protos-1.58.0 grpc-google-iam-v1-0.12.6 grpcio-1.51.1 grpcio-status-1.48.2 h5py-3.7.0 hdfs-2.7.0 httplib2-0.20.4 importlib-metadata-6.0.0 importlib-resources-5.10.2 ipykernel-6.20.2 ipython-7.34.0 ipython-genutils-0.2.0 ipywidgets-7.7.2 isoduration-20.11.0 itsdangerous-2.1.2 jedi-0.18.2 jinja2-3.1.2 joblib-1.2.0 jsonpointer-2.3 jsonschema-4.17.3 jupyter-1.0.0 jupyter-client-7.4.9 jupyter-console-6.4.4 jupyter-core-5.1.3 jupyter-events-0.6.3 jupyter-server-2.1.0 jupyter-server-terminals-0.4.4 jupyterlab-pygments-0.2.2 jupyterlab-widgets-1.1.1 keras-2.10.0 keras-preprocessing-1.1.2 keras-tuner-1.1.3 kt-legacy-1.0.4 kubernetes-12.0.1 libclang-15.0.6.1 markdown-3.4.1 matplotlib-inline-0.1.6 mistune-2.0.4 ml-metadata-1.11.0 ml-pipelines-sdk-1.11.0 nbclassic-0.4.8 nbclient-0.7.2 nbconvert-7.2.8 nbformat-5.7.3 nest-asyncio-1.5.6 notebook-6.5.2 notebook-shim-0.2.2 numpy-1.22.4 oauth2client-4.1.3 oauthlib-3.2.2 objsize-0.6.1 opt-einsum-3.3.0 orjson-3.8.5 overrides-6.5.0 packaging-20.9 pandas-1.5.3 pandocfilters-1.5.0 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 pkgutil-resolve-name-1.3.10 platformdirs-2.6.2 portpicker-1.5.2 prometheus-client-0.15.0 prompt-toolkit-3.0.36 proto-plus-1.22.2 protobuf-3.19.6 psutil-5.9.4 ptyprocess-0.7.0 pyarrow-6.0.1 pyasn1-0.4.8 pyasn1-modules-0.2.8 pydot-1.4.2 pyfarmhash-0.3.2 pygments-2.14.0 pymongo-3.13.0 pyparsing-3.0.9 pyrsistent-0.19.3 python-dateutil-2.8.2 python-json-logger-2.0.4 pytz-2022.7.1 pyyaml-5.4.1 pyzmq-25.0.0 qtconsole-5.4.0 qtpy-2.3.0 regex-2022.10.31 requests-oauthlib-1.3.1 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rsa-4.9 scikit-learn-1.2.0 scipy-1.10.0 six-1.16.0 sniffio-1.3.0 soupsieve-2.3.2.post1 sqlparse-0.4.3 tensorboard-2.10.1 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.10.1 tensorflow-data-validation-1.11.0 tensorflow-estimator-2.10.0 tensorflow-hub-0.12.0 tensorflow-io-gcs-filesystem-0.29.0 tensorflow-metadata-1.11.0 tensorflow-model-analysis-0.42.0 tensorflow-serving-api-2.10.1 tensorflow-transform-1.11.0 termcolor-2.2.0 terminado-0.17.1 tfx-1.11.0 tfx-bsl-1.11.0 threadpoolctl-3.1.0 tinycss2-1.2.1 tornado-6.2 traitlets-5.8.1 typing-extensions-4.4.0 uri-template-1.2.0 uritemplate-3.0.1 wcwidth-0.2.6 webcolors-1.12 webencodings-0.5.1 websocket-client-1.4.2 widgetsnbextension-3.6.1 wrapt-1.14.1 zipp-3.11.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "dateutil",
                  "google",
                  "ipython_genutils",
                  "pexpect",
                  "pickleshare"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install jupyter scikit-learn tensorflow tfx==1.11.0 flask joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpqsJV1nsw7t",
        "outputId": "b9e96084-5212-4a40-8545-1a5ed09e0010"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  Stroke-Disease-Detection.zip\n",
            "   creating: modules/\n",
            "  inflating: modules/components.py   \n",
            "  inflating: modules/trainer.py      \n",
            "  inflating: modules/transform.py    \n",
            "  inflating: modules/tuner.py        \n",
            "   creating: monitoring/\n",
            "  inflating: monitoring/Dockerfile   \n",
            "  inflating: monitoring/prometheus.yml  \n",
            " extracting: Dockerfile              \n",
            "   creating: config/\n",
            "  inflating: config/prometheus.config  \n",
            "   creating: images/\n"
          ]
        }
      ],
      "source": [
        "!unzip Stroke-Disease-Detection.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "j_mJt78ccznN"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from typing import Text\n",
        "\n",
        "from absl import logging\n",
        "from tfx.orchestration import metadata, pipeline\n",
        "from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner\n",
        "from modules import components"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_695taudToL"
      },
      "source": [
        "# **2. Data Loading**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsCH52-DhLIL"
      },
      "source": [
        "## 2.1 Environment and Kaggle Credential"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cu4vE3L4J1cf"
      },
      "source": [
        "Set up the [Colab](https://colab.research.google.com) `operating system` environment with the `KAGGLE_USERNAME` variable and the `KAGGLE_KEY` variable to connect to the [Kaggle](https://kaggle.com) platform using [Kaggle's Beta API](https://www.kaggle.com/docs/api) Token."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "S4RK1wiHe6UM"
      },
      "outputs": [],
      "source": [
        "os.environ['KAGGLE_USERNAME'] = 'andrewbjamesie'\n",
        "os.environ['KAGGLE_KEY']      = 'b75c236a492526b84d0dc7517c37d48b'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Msc4IG9hUbA"
      },
      "source": [
        "## 2.2 Dataset Download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tfGCMcvLhT8L"
      },
      "source": [
        "Download the dataset form Kaggle with the dataset file name, `healthcare-dataset-stroke-data.csv`. The dataset used in this project is the [Stroke Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset) dataset in the form of a `.csv` ([Comma-separated Values](https://en.wikipedia.org/wiki/Comma-separated_values)) file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYO1ImDahIZN",
        "outputId": "c4b4c282-bdba-4413-bf0b-1ddbe3b7fcd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading healthcare-dataset-stroke-data.csv to /content\n",
            "\r  0% 0.00/310k [00:00<?, ?B/s]\n",
            "\r100% 310k/310k [00:00<00:00, 78.7MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d fedesoriano/stroke-prediction-dataset -f healthcare-dataset-stroke-data.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNgt1eSiZs1g"
      },
      "source": [
        "## 2.3 Dataset Preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Xaa56p_2e_g6"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
        "df = df.drop('id', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yyb9l4Z2ZPb2",
        "outputId": "5186a5fd-2153-4652-ecdb-ec6c2fa2d6de"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUX4LgWjaAmV",
        "outputId": "56c0f9af-1c0c-4c07-96f9-af2584acd206"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "gender               0\n",
              "age                  0\n",
              "hypertension         0\n",
              "heart_disease        0\n",
              "ever_married         0\n",
              "work_type            0\n",
              "Residence_type       0\n",
              "avg_glucose_level    0\n",
              "bmi                  0\n",
              "smoking_status       0\n",
              "stroke               0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = df.dropna()\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYJ4qsHbaT_3",
        "outputId": "7e6fe3f7-896f-4f70-bb72-91a4970d5d8f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4909 entries, 0 to 5109\n",
            "Data columns (total 11 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   gender             4909 non-null   object \n",
            " 1   age                4909 non-null   float64\n",
            " 2   hypertension       4909 non-null   int64  \n",
            " 3   heart_disease      4909 non-null   int64  \n",
            " 4   ever_married       4909 non-null   object \n",
            " 5   work_type          4909 non-null   object \n",
            " 6   Residence_type     4909 non-null   object \n",
            " 7   avg_glucose_level  4909 non-null   float64\n",
            " 8   bmi                4909 non-null   float64\n",
            " 9   smoking_status     4909 non-null   object \n",
            " 10  stroke             4909 non-null   int64  \n",
            "dtypes: float64(3), int64(3), object(5)\n",
            "memory usage: 460.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Qg_etYDeaSTh"
      },
      "outputs": [],
      "source": [
        "df['age'] = df['age'].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "gO0iOIyLbRDv",
        "outputId": "13683978-8b9c-48a2-c2d6-1aa3cc8ee935"
      },
      "outputs": [
        {
          "data": {
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
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              "      <th>Residence_type</th>\n",
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              "      <th>bmi</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Male</td>\n",
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              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>Rural</td>\n",
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              "      <td>32.5</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>79</td>\n",
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              "      <td>Self-employed</td>\n",
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              "      <td>174.12</td>\n",
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              "      <td>never smoked</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Rural</td>\n",
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              "      <th>5106</th>\n",
              "      <td>Female</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Urban</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>82.99</td>\n",
              "      <td>30.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>Male</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>Female</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>85.28</td>\n",
              "      <td>26.2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4909 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b320024-ee06-471c-a3a5-97144f9cd66c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2b320024-ee06-471c-a3a5-97144f9cd66c 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-2b320024-ee06-471c-a3a5-97144f9cd66c');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      gender  age  hypertension  heart_disease ever_married      work_type  \\\n",
              "0       Male   67             0              1          Yes        Private   \n",
              "2       Male   80             0              1          Yes        Private   \n",
              "3     Female   49             0              0          Yes        Private   \n",
              "4     Female   79             1              0          Yes  Self-employed   \n",
              "5       Male   81             0              0          Yes        Private   \n",
              "...      ...  ...           ...            ...          ...            ...   \n",
              "5104  Female   13             0              0           No       children   \n",
              "5106  Female   81             0              0          Yes  Self-employed   \n",
              "5107  Female   35             0              0          Yes  Self-employed   \n",
              "5108    Male   51             0              0          Yes        Private   \n",
              "5109  Female   44             0              0          Yes       Govt_job   \n",
              "\n",
              "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
              "0             Urban             228.69  36.6  formerly smoked       1  \n",
              "2             Rural             105.92  32.5     never smoked       1  \n",
              "3             Urban             171.23  34.4           smokes       1  \n",
              "4             Rural             174.12  24.0     never smoked       1  \n",
              "5             Urban             186.21  29.0  formerly smoked       1  \n",
              "...             ...                ...   ...              ...     ...  \n",
              "5104          Rural             103.08  18.6          Unknown       0  \n",
              "5106          Urban             125.20  40.0     never smoked       0  \n",
              "5107          Rural              82.99  30.6     never smoked       0  \n",
              "5108          Rural             166.29  25.6  formerly smoked       0  \n",
              "5109          Urban              85.28  26.2          Unknown       0  \n",
              "\n",
              "[4909 rows x 11 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "DATA_PATH = 'data'\n",
        "\n",
        "if not os.path.exists(DATA_PATH):\n",
        "    os.makedirs(DATA_PATH)\n",
        "\n",
        "df.to_csv(os.path.join(DATA_PATH, 'healthcare-dataset-stroke-data.csv'), index=False)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTPMUlyUdR-M"
      },
      "source": [
        "# **3. Set Pipeline Variable**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "30gNVCymdTUL"
      },
      "outputs": [],
      "source": [
        "PIPELINE_NAME = 'stroke-disease-pipeline'\n",
        "\n",
        "# Pipeline inputs\n",
        "DATA_ROOT = 'data'\n",
        "TRANSFORM_MODULE_FILE = 'modules/transform.py'\n",
        "TUNER_MODULE_FILE = 'modules/tuner.py'\n",
        "TRAINER_MODULE_FILE = 'modules/trainer.py'\n",
        "\n",
        "# Pipeline outputs\n",
        "OUTPUT_BASE = 'outputs'\n",
        "\n",
        "serving_model_dir = os.path.join(OUTPUT_BASE, 'serving_model')\n",
        "pipeline_root = os.path.join(OUTPUT_BASE, PIPELINE_NAME)\n",
        "metadata_path = os.path.join(pipeline_root, 'metadata.sqlite')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sHcCOTDv5SV"
      },
      "source": [
        "# **4. Pipeline Initialization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "SI8vOQDEvjE5"
      },
      "outputs": [],
      "source": [
        "def init_local_pipeline(\n",
        "    components, pipeline_root: Text\n",
        ") -> pipeline.Pipeline:\n",
        "    \"\"\"Init local pipeline\n",
        "\n",
        "    Args:\n",
        "        components (dict): tfx components\n",
        "        pipeline_root (Text): path to pipeline directory\n",
        "\n",
        "    Returns:\n",
        "        pipeline.Pipeline: apache beam pipeline orchestration\n",
        "    \"\"\"\n",
        "    logging.info(f\"Pipeline root set to: {pipeline_root}\")\n",
        "    beam_args = [\n",
        "        '--direct_running_mode=multi_processing'\n",
        "        # 0 auto-detect based on on the number of CPUs available\n",
        "        # during execution time.\n",
        "        '----direct_num_workers=0'\n",
        "    ]\n",
        "\n",
        "    return pipeline.Pipeline(\n",
        "        pipeline_name=PIPELINE_NAME,\n",
        "        pipeline_root=pipeline_root,\n",
        "        components=components,\n",
        "        enable_cache=True,\n",
        "        metadata_connection_config=metadata.sqlite_metadata_connection_config(\n",
        "            metadata_path\n",
        "        ),\n",
        "        eam_pipeline_args=beam_args\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K_QaH3t9wU2T",
        "outputId": "3bc3544a-e139-4f47-dc68-22ef0437ed54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trial 30 Complete [00h 04m 23s]\n",
            "val_loss: 0.30945828557014465\n",
            "\n",
            "Best val_loss So Far: 0.15500961244106293\n",
            "Total elapsed time: 01h 00m 49s\n",
            "INFO:tensorflow:Oracle triggered exit\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n",
            "INFO:absl:Finished tuning... Tuner ID: tuner0\n",
            "INFO:absl:Best HyperParameters: {'space': [{'class_name': 'Choice', 'config': {'name': 'num_layers', 'default': 1, 'conditions': [], 'values': [1, 2, 3], 'ordered': True}}, {'class_name': 'Int', 'config': {'name': 'dense_units', 'default': None, 'conditions': [], 'min_value': 16, 'max_value': 256, 'step': 16, 'sampling': None}}, {'class_name': 'Float', 'config': {'name': 'dropout_rate', 'default': 0.1, 'conditions': [], 'min_value': 0.1, 'max_value': 0.7, 'step': 0.1, 'sampling': None}}, {'class_name': 'Choice', 'config': {'name': 'learning_rate', 'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}}], 'values': {'num_layers': 1, 'dense_units': 48, 'dropout_rate': 0.6, 'learning_rate': 0.0001, 'tuner/epochs': 4, 'tuner/initial_epoch': 2, 'tuner/bracket': 2, 'tuner/round': 1, 'tuner/trial_id': '0003'}}\n",
            "INFO:absl:Best Hyperparameters are written to outputs/stroke-disease-pipeline/Tuner/best_hyperparameters/7/best_hyperparameters.txt.\n",
            "INFO:absl:Tuner results are written to outputs/stroke-disease-pipeline/Tuner/tuner_results/7/tuner_results.json.\n",
            "INFO:absl:Cleaning up stateless execution info.\n",
            "INFO:absl:Execution 7 succeeded.\n",
            "INFO:absl:Cleaning up stateful execution info.\n",
            "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'tuner_results': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Tuner/tuner_results/7\"\n",
            ", artifact_type: name: \"TunerResults\"\n",
            ")], 'best_hyperparameters': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Tuner/best_hyperparameters/7\"\n",
            ", artifact_type: name: \"HyperParameters\"\n",
            ")]}) for execution 7\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:node Tuner is finished.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Results summary\n",
            "Results in outputs/stroke-disease-pipeline/Tuner/.system/executor_execution/7/.temp/7/stroke_disaster_kt\n",
            "Showing 10 best trials\n",
            "<keras_tuner.engine.objective.Objective object at 0x7ff256dcdc40>\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 1\n",
            "dense_units: 48\n",
            "dropout_rate: 0.6\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0003\n",
            "Score: 0.15500961244106293\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 2\n",
            "dense_units: 48\n",
            "dropout_rate: 0.2\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 2\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 2\n",
            "tuner/round: 0\n",
            "Score: 0.15590107440948486\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 1\n",
            "dense_units: 48\n",
            "dropout_rate: 0.6\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 10\n",
            "tuner/initial_epoch: 4\n",
            "tuner/bracket: 2\n",
            "tuner/round: 2\n",
            "tuner/trial_id: 0013\n",
            "Score: 0.1566063016653061\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 2\n",
            "dense_units: 48\n",
            "dropout_rate: 0.2\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 10\n",
            "tuner/initial_epoch: 4\n",
            "tuner/bracket: 2\n",
            "tuner/round: 2\n",
            "tuner/trial_id: 0012\n",
            "Score: 0.1580609381198883\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 1\n",
            "dense_units: 48\n",
            "dropout_rate: 0.6\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 2\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 2\n",
            "tuner/round: 0\n",
            "Score: 0.1581641137599945\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 1\n",
            "dense_units: 240\n",
            "dropout_rate: 0.30000000000000004\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 10\n",
            "tuner/initial_epoch: 4\n",
            "tuner/bracket: 1\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0018\n",
            "Score: 0.15890902280807495\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 2\n",
            "dense_units: 48\n",
            "dropout_rate: 0.2\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0000\n",
            "Score: 0.15927976369857788\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 3\n",
            "dense_units: 80\n",
            "dropout_rate: 0.5\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0004\n",
            "Score: 0.16006919741630554\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 2\n",
            "dense_units: 112\n",
            "dropout_rate: 0.5\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0002\n",
            "Score: 0.1611279994249344\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 1\n",
            "dense_units: 240\n",
            "dropout_rate: 0.30000000000000004\n",
            "learning_rate: 0.0001\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 1\n",
            "tuner/round: 0\n",
            "Score: 0.16344670951366425\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:absl:node Trainer is running.\n",
            "INFO:absl:Running launcher for node_info {\n",
            "  type {\n",
            "    name: \"tfx.components.trainer.component.Trainer\"\n",
            "    base_type: TRAIN\n",
            "  }\n",
            "  id: \"Trainer\"\n",
            "}\n",
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            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline.Trainer\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "inputs {\n",
            "  inputs {\n",
            "    key: \"examples\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Transform\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
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            "          }\n",
            "          name {\n",
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            "          }\n",
            "        }\n",
            "        context_queries {\n",
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            "            }\n",
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            "        artifact_query {\n",
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            "            base_type: DATASET\n",
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            "        output_key: \"transformed_examples\"\n",
            "      }\n",
            "      min_count: 1\n",
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            "  }\n",
            "  inputs {\n",
            "    key: \"hyperparameters\"\n",
            "    value {\n",
            "      channels {\n",
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            "          id: \"Tuner\"\n",
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            "        context_queries {\n",
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            "          name {\n",
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            "          }\n",
            "        }\n",
            "        context_queries {\n",
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            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Tuner\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"HyperParameters\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"best_hyperparameters\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"schema\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"SchemaGen\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.SchemaGen\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Schema\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"schema\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"transform_graph\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Transform\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Transform\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"TransformGraph\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"transform_graph\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "outputs {\n",
            "  outputs {\n",
            "    key: \"model\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"Model\"\n",
            "          base_type: MODEL\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  outputs {\n",
            "    key: \"model_run\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"ModelRun\"\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "parameters {\n",
            "  parameters {\n",
            "    key: \"custom_config\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"eval_args\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"num_steps\\\": 1000,\\n  \\\"splits\\\": [\\n    \\\"eval\\\"\\n  ]\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"module_path\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"trainer@outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"train_args\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"num_steps\\\": 5000,\\n  \\\"splits\\\": [\\n    \\\"train\\\"\\n  ]\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "upstream_nodes: \"SchemaGen\"\n",
            "upstream_nodes: \"Transform\"\n",
            "upstream_nodes: \"Tuner\"\n",
            "downstream_nodes: \"Evaluator\"\n",
            "downstream_nodes: \"Pusher\"\n",
            "execution_options {\n",
            "  caching_options {\n",
            "    enable_cache: true\n",
            "  }\n",
            "}\n",
            "\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "INFO:absl:[Trainer] Resolved inputs: ({'schema': [Artifact(artifact: id: 3\n",
            "type_id: 20\n",
            "uri: \"outputs/stroke-disease-pipeline/SchemaGen/schema/4\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290227927\n",
            "last_update_time_since_epoch: 1674290227927\n",
            ", artifact_type: id: 20\n",
            "name: \"Schema\"\n",
            ")], 'hyperparameters': [Artifact(artifact: id: 14\n",
            "type_id: 28\n",
            "uri: \"outputs/stroke-disease-pipeline/Tuner/best_hyperparameters/7\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674293929776\n",
            "last_update_time_since_epoch: 1674293929776\n",
            ", artifact_type: id: 28\n",
            "name: \"HyperParameters\"\n",
            ")], 'transform_graph': [Artifact(artifact: id: 7\n",
            "type_id: 25\n",
            "uri: \"outputs/stroke-disease-pipeline/Transform/transform_graph/6\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290273756\n",
            "last_update_time_since_epoch: 1674290273756\n",
            ", artifact_type: id: 25\n",
            "name: \"TransformGraph\"\n",
            ")], 'examples': [Artifact(artifact: id: 12\n",
            "type_id: 15\n",
            "uri: \"outputs/stroke-disease-pipeline/Transform/transformed_examples/6\"\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value {\n",
            "    string_value: \"[\\\"eval\\\", \\\"train\\\"]\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290273757\n",
            "last_update_time_since_epoch: 1674290273757\n",
            ", artifact_type: id: 15\n",
            "name: \"Examples\"\n",
            "properties {\n",
            "  key: \"span\"\n",
            "  value: INT\n",
            "}\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value: STRING\n",
            "}\n",
            "properties {\n",
            "  key: \"version\"\n",
            "  value: INT\n",
            "}\n",
            "base_type: DATASET\n",
            ")]},)\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:Going to run a new execution 8\n",
            "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=8, input_dict={'schema': [Artifact(artifact: id: 3\n",
            "type_id: 20\n",
            "uri: \"outputs/stroke-disease-pipeline/SchemaGen/schema/4\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290227927\n",
            "last_update_time_since_epoch: 1674290227927\n",
            ", artifact_type: id: 20\n",
            "name: \"Schema\"\n",
            ")], 'hyperparameters': [Artifact(artifact: id: 14\n",
            "type_id: 28\n",
            "uri: \"outputs/stroke-disease-pipeline/Tuner/best_hyperparameters/7\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674293929776\n",
            "last_update_time_since_epoch: 1674293929776\n",
            ", artifact_type: id: 28\n",
            "name: \"HyperParameters\"\n",
            ")], 'transform_graph': [Artifact(artifact: id: 7\n",
            "type_id: 25\n",
            "uri: \"outputs/stroke-disease-pipeline/Transform/transform_graph/6\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290273756\n",
            "last_update_time_since_epoch: 1674290273756\n",
            ", artifact_type: id: 25\n",
            "name: \"TransformGraph\"\n",
            ")], 'examples': [Artifact(artifact: id: 12\n",
            "type_id: 15\n",
            "uri: \"outputs/stroke-disease-pipeline/Transform/transformed_examples/6\"\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value {\n",
            "    string_value: \"[\\\"eval\\\", \\\"train\\\"]\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290273757\n",
            "last_update_time_since_epoch: 1674290273757\n",
            ", artifact_type: id: 15\n",
            "name: \"Examples\"\n",
            "properties {\n",
            "  key: \"span\"\n",
            "  value: INT\n",
            "}\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value: STRING\n",
            "}\n",
            "properties {\n",
            "  key: \"version\"\n",
            "  value: INT\n",
            "}\n",
            "base_type: DATASET\n",
            ")]}, output_dict=defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            ", artifact_type: name: \"Model\"\n",
            "base_type: MODEL\n",
            ")], 'model_run': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Trainer/model_run/8\"\n",
            ", artifact_type: name: \"ModelRun\"\n",
            ")]}), exec_properties={'train_args': '{\\n  \"num_steps\": 5000,\\n  \"splits\": [\\n    \"train\"\\n  ]\\n}', 'module_path': 'trainer@outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl', 'custom_config': 'null', 'eval_args': '{\\n  \"num_steps\": 1000,\\n  \"splits\": [\\n    \"eval\"\\n  ]\\n}'}, execution_output_uri='outputs/stroke-disease-pipeline/Trainer/.system/executor_execution/8/executor_output.pb', stateful_working_dir='outputs/stroke-disease-pipeline/Trainer/.system/stateful_working_dir/20230121-083653.609845', tmp_dir='outputs/stroke-disease-pipeline/Trainer/.system/executor_execution/8/.temp/', pipeline_node=node_info {\n",
            "  type {\n",
            "    name: \"tfx.components.trainer.component.Trainer\"\n",
            "    base_type: TRAIN\n",
            "  }\n",
            "  id: \"Trainer\"\n",
            "}\n",
            "contexts {\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline_run\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"20230121-083653.609845\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"node\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline.Trainer\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "inputs {\n",
            "  inputs {\n",
            "    key: \"examples\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Transform\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Transform\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Examples\"\n",
            "            base_type: DATASET\n",
            "          }\n",
            "        }\n",
            "        output_key: \"transformed_examples\"\n",
            "      }\n",
            "      min_count: 1\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"hyperparameters\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Tuner\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Tuner\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"HyperParameters\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"best_hyperparameters\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"schema\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"SchemaGen\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.SchemaGen\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Schema\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"schema\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"transform_graph\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Transform\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Transform\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"TransformGraph\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"transform_graph\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "outputs {\n",
            "  outputs {\n",
            "    key: \"model\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"Model\"\n",
            "          base_type: MODEL\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  outputs {\n",
            "    key: \"model_run\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"ModelRun\"\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "parameters {\n",
            "  parameters {\n",
            "    key: \"custom_config\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"eval_args\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"num_steps\\\": 1000,\\n  \\\"splits\\\": [\\n    \\\"eval\\\"\\n  ]\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"module_path\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"trainer@outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"train_args\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"num_steps\\\": 5000,\\n  \\\"splits\\\": [\\n    \\\"train\\\"\\n  ]\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "upstream_nodes: \"SchemaGen\"\n",
            "upstream_nodes: \"Transform\"\n",
            "upstream_nodes: \"Tuner\"\n",
            "downstream_nodes: \"Evaluator\"\n",
            "downstream_nodes: \"Pusher\"\n",
            "execution_options {\n",
            "  caching_options {\n",
            "    enable_cache: true\n",
            "  }\n",
            "}\n",
            ", pipeline_info=id: \"stroke-disease-pipeline\"\n",
            ", pipeline_run_id='20230121-083653.609845')\n",
            "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n",
            "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n",
            "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n",
            "INFO:absl:udf_utils.get_fn {'train_args': '{\\n  \"num_steps\": 5000,\\n  \"splits\": [\\n    \"train\"\\n  ]\\n}', 'module_path': 'trainer@outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl', 'custom_config': 'null', 'eval_args': '{\\n  \"num_steps\": 1000,\\n  \"splits\": [\\n    \"eval\"\\n  ]\\n}'} 'run_fn'\n",
            "INFO:absl:Installing 'outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl' to a temporary directory.\n",
            "INFO:absl:Executing: ['/usr/bin/python3.real', '-m', 'pip', 'install', '--target', '/tmp/tmpqquhqy2h', 'outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl']\n",
            "INFO:absl:Successfully installed 'outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl'.\n",
            "INFO:absl:Training model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " gender_xf (InputLayer)         [(None, 3)]          0           []                               \n",
            "                                                                                                  \n",
            " ever_married_xf (InputLayer)   [(None, 3)]          0           []                               \n",
            "                                                                                                  \n",
            " work_type_xf (InputLayer)      [(None, 6)]          0           []                               \n",
            "                                                                                                  \n",
            " Residence_type_xf (InputLayer)  [(None, 3)]         0           []                               \n",
            "                                                                                                  \n",
            " smoking_status_xf (InputLayer)  [(None, 5)]         0           []                               \n",
            "                                                                                                  \n",
            " age_xf (InputLayer)            [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " hypertension_xf (InputLayer)   [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " heart_disease_xf (InputLayer)  [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " avg_glucose_level_xf (InputLay  [(None, 1)]         0           []                               \n",
            " er)                                                                                              \n",
            "                                                                                                  \n",
            " bmi_xf (InputLayer)            [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 25)           0           ['gender_xf[0][0]',              \n",
            "                                                                  'ever_married_xf[0][0]',        \n",
            "                                                                  'work_type_xf[0][0]',           \n",
            "                                                                  'Residence_type_xf[0][0]',      \n",
            "                                                                  'smoking_status_xf[0][0]',      \n",
            "                                                                  'age_xf[0][0]',                 \n",
            "                                                                  'hypertension_xf[0][0]',        \n",
            "                                                                  'heart_disease_xf[0][0]',       \n",
            "                                                                  'avg_glucose_level_xf[0][0]',   \n",
            "                                                                  'bmi_xf[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 48)           1248        ['concatenate_1[0][0]']          \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 48)           2352        ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 48)           0           ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 1)            49          ['dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3,649\n",
            "Trainable params: 3,649\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/2\n",
            "4996/5000 [============================>.] - ETA: 0s - loss: 0.2097 - binary_accuracy: 0.9367\n",
            "Epoch 1: val_binary_accuracy improved from -inf to 0.95195, saving model to outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:Function `_wrapped_model` contains input name(s) Residence_type_xf with unsupported characters which will be renamed to residence_type_xf in the SavedModel.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving/assets\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving/assets\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r5000/5000 [==============================] - 21s 4ms/step - loss: 0.2097 - binary_accuracy: 0.9367 - val_loss: 0.1636 - val_binary_accuracy: 0.9520\n",
            "Epoch 2/2\n",
            "4990/5000 [============================>.] - ETA: 0s - loss: 0.1509 - binary_accuracy: 0.9588\n",
            "Epoch 2: val_binary_accuracy did not improve from 0.95195\n",
            "5000/5000 [==============================] - 19s 4ms/step - loss: 0.1510 - binary_accuracy: 0.9588 - val_loss: 0.1580 - val_binary_accuracy: 0.9519\n",
            "INFO:tensorflow:struct2tensor is not available.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:struct2tensor is not available.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:tensorflow_text is not available.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:tensorflow_text is not available.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving/assets\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving/assets\n",
            "INFO:absl:Training complete. Model written to outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving. ModelRun written to outputs/stroke-disease-pipeline/Trainer/model_run/8\n",
            "INFO:absl:Cleaning up stateless execution info.\n",
            "INFO:absl:Execution 8 succeeded.\n",
            "INFO:absl:Cleaning up stateful execution info.\n",
            "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            ", artifact_type: name: \"Model\"\n",
            "base_type: MODEL\n",
            ")], 'model_run': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Trainer/model_run/8\"\n",
            ", artifact_type: name: \"ModelRun\"\n",
            ")]}) for execution 8\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:node Trainer is finished.\n",
            "INFO:absl:node Evaluator is running.\n",
            "INFO:absl:Running launcher for node_info {\n",
            "  type {\n",
            "    name: \"tfx.components.evaluator.component.Evaluator\"\n",
            "    base_type: EVALUATE\n",
            "  }\n",
            "  id: \"Evaluator\"\n",
            "}\n",
            "contexts {\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline_run\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"20230121-083653.609845\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"node\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline.Evaluator\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "inputs {\n",
            "  inputs {\n",
            "    key: \"baseline_model\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Latest_blessed_model_resolver\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Latest_blessed_model_resolver\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Model\"\n",
            "            base_type: MODEL\n",
            "          }\n",
            "        }\n",
            "        output_key: \"model\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"examples\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"CsvExampleGen\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.CsvExampleGen\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Examples\"\n",
            "            base_type: DATASET\n",
            "          }\n",
            "        }\n",
            "        output_key: \"examples\"\n",
            "      }\n",
            "      min_count: 1\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"model\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Trainer\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Trainer\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Model\"\n",
            "            base_type: MODEL\n",
            "          }\n",
            "        }\n",
            "        output_key: \"model\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "outputs {\n",
            "  outputs {\n",
            "    key: \"blessing\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"ModelBlessing\"\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  outputs {\n",
            "    key: \"evaluation\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"ModelEvaluation\"\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "parameters {\n",
            "  parameters {\n",
            "    key: \"eval_config\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"metrics_specs\\\": [\\n    {\\n      \\\"metrics\\\": [\\n        {\\n          \\\"class_name\\\": \\\"AUC\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"Precision\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"Recall\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"ExampleCount\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"TruePositives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"FalsePositives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"TrueNegatives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"FalseNegatives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"BinaryAccuracy\\\",\\n          \\\"threshold\\\": {\\n            \\\"change_threshold\\\": {\\n              \\\"absolute\\\": 0.0001,\\n              \\\"direction\\\": \\\"HIGHER_IS_BETTER\\\"\\n            },\\n            \\\"value_threshold\\\": {\\n              \\\"lower_bound\\\": 0.5\\n            }\\n          }\\n        }\\n      ]\\n    }\\n  ],\\n  \\\"model_specs\\\": [\\n    {\\n      \\\"label_key\\\": \\\"stroke\\\"\\n    }\\n  ],\\n  \\\"slicing_specs\\\": [\\n    {},\\n    {\\n      \\\"feature_keys\\\": [\\n        \\\"gender\\\",\\n        \\\"ever_married\\\"\\n      ]\\n    }\\n  ]\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"example_splits\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"fairness_indicator_thresholds\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "upstream_nodes: \"CsvExampleGen\"\n",
            "upstream_nodes: \"Latest_blessed_model_resolver\"\n",
            "upstream_nodes: \"Trainer\"\n",
            "downstream_nodes: \"Pusher\"\n",
            "execution_options {\n",
            "  caching_options {\n",
            "    enable_cache: true\n",
            "  }\n",
            "}\n",
            "\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "INFO:absl:[Evaluator] Resolved inputs: ({'examples': [Artifact(artifact: id: 1\n",
            "type_id: 15\n",
            "uri: \"outputs/stroke-disease-pipeline/CsvExampleGen/examples/1\"\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value {\n",
            "    string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"file_format\"\n",
            "  value {\n",
            "    string_value: \"tfrecords_gzip\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"input_fingerprint\"\n",
            "  value {\n",
            "    string_value: \"split:single_split,num_files:1,total_bytes:276776,xor_checksum:1674290211,sum_checksum:1674290211\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"payload_format\"\n",
            "  value {\n",
            "    string_value: \"FORMAT_TF_EXAMPLE\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"span\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290219063\n",
            "last_update_time_since_epoch: 1674290219063\n",
            ", artifact_type: id: 15\n",
            "name: \"Examples\"\n",
            "properties {\n",
            "  key: \"span\"\n",
            "  value: INT\n",
            "}\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value: STRING\n",
            "}\n",
            "properties {\n",
            "  key: \"version\"\n",
            "  value: INT\n",
            "}\n",
            "base_type: DATASET\n",
            ")], 'baseline_model': [], 'model': [Artifact(artifact: id: 15\n",
            "type_id: 30\n",
            "uri: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674293981324\n",
            "last_update_time_since_epoch: 1674293981324\n",
            ", artifact_type: id: 30\n",
            "name: \"Model\"\n",
            "base_type: MODEL\n",
            ")]},)\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:Going to run a new execution 9\n",
            "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=9, input_dict={'examples': [Artifact(artifact: id: 1\n",
            "type_id: 15\n",
            "uri: \"outputs/stroke-disease-pipeline/CsvExampleGen/examples/1\"\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value {\n",
            "    string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"file_format\"\n",
            "  value {\n",
            "    string_value: \"tfrecords_gzip\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"input_fingerprint\"\n",
            "  value {\n",
            "    string_value: \"split:single_split,num_files:1,total_bytes:276776,xor_checksum:1674290211,sum_checksum:1674290211\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"payload_format\"\n",
            "  value {\n",
            "    string_value: \"FORMAT_TF_EXAMPLE\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"span\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674290219063\n",
            "last_update_time_since_epoch: 1674290219063\n",
            ", artifact_type: id: 15\n",
            "name: \"Examples\"\n",
            "properties {\n",
            "  key: \"span\"\n",
            "  value: INT\n",
            "}\n",
            "properties {\n",
            "  key: \"split_names\"\n",
            "  value: STRING\n",
            "}\n",
            "properties {\n",
            "  key: \"version\"\n",
            "  value: INT\n",
            "}\n",
            "base_type: DATASET\n",
            ")], 'baseline_model': [], 'model': [Artifact(artifact: id: 15\n",
            "type_id: 30\n",
            "uri: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674293981324\n",
            "last_update_time_since_epoch: 1674293981324\n",
            ", artifact_type: id: 30\n",
            "name: \"Model\"\n",
            "base_type: MODEL\n",
            ")]}, output_dict=defaultdict(<class 'list'>, {'blessing': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Evaluator/blessing/9\"\n",
            ", artifact_type: name: \"ModelBlessing\"\n",
            ")], 'evaluation': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Evaluator/evaluation/9\"\n",
            ", artifact_type: name: \"ModelEvaluation\"\n",
            ")]}), exec_properties={'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"metrics\": [\\n        {\\n          \"class_name\": \"AUC\"\\n        },\\n        {\\n          \"class_name\": \"Precision\"\\n        },\\n        {\\n          \"class_name\": \"Recall\"\\n        },\\n        {\\n          \"class_name\": \"ExampleCount\"\\n        },\\n        {\\n          \"class_name\": \"TruePositives\"\\n        },\\n        {\\n          \"class_name\": \"FalsePositives\"\\n        },\\n        {\\n          \"class_name\": \"TrueNegatives\"\\n        },\\n        {\\n          \"class_name\": \"FalseNegatives\"\\n        },\\n        {\\n          \"class_name\": \"BinaryAccuracy\",\\n          \"threshold\": {\\n            \"change_threshold\": {\\n              \"absolute\": 0.0001,\\n              \"direction\": \"HIGHER_IS_BETTER\"\\n            },\\n            \"value_threshold\": {\\n              \"lower_bound\": 0.5\\n            }\\n          }\\n        }\\n      ]\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"stroke\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"gender\",\\n        \"ever_married\"\\n      ]\\n    }\\n  ]\\n}'}, execution_output_uri='outputs/stroke-disease-pipeline/Evaluator/.system/executor_execution/9/executor_output.pb', stateful_working_dir='outputs/stroke-disease-pipeline/Evaluator/.system/stateful_working_dir/20230121-083653.609845', tmp_dir='outputs/stroke-disease-pipeline/Evaluator/.system/executor_execution/9/.temp/', pipeline_node=node_info {\n",
            "  type {\n",
            "    name: \"tfx.components.evaluator.component.Evaluator\"\n",
            "    base_type: EVALUATE\n",
            "  }\n",
            "  id: \"Evaluator\"\n",
            "}\n",
            "contexts {\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline_run\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"20230121-083653.609845\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"node\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline.Evaluator\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "inputs {\n",
            "  inputs {\n",
            "    key: \"baseline_model\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Latest_blessed_model_resolver\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Latest_blessed_model_resolver\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Model\"\n",
            "            base_type: MODEL\n",
            "          }\n",
            "        }\n",
            "        output_key: \"model\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"examples\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"CsvExampleGen\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.CsvExampleGen\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Examples\"\n",
            "            base_type: DATASET\n",
            "          }\n",
            "        }\n",
            "        output_key: \"examples\"\n",
            "      }\n",
            "      min_count: 1\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"model\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Trainer\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Trainer\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Model\"\n",
            "            base_type: MODEL\n",
            "          }\n",
            "        }\n",
            "        output_key: \"model\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "outputs {\n",
            "  outputs {\n",
            "    key: \"blessing\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"ModelBlessing\"\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  outputs {\n",
            "    key: \"evaluation\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"ModelEvaluation\"\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "parameters {\n",
            "  parameters {\n",
            "    key: \"eval_config\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"metrics_specs\\\": [\\n    {\\n      \\\"metrics\\\": [\\n        {\\n          \\\"class_name\\\": \\\"AUC\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"Precision\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"Recall\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"ExampleCount\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"TruePositives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"FalsePositives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"TrueNegatives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"FalseNegatives\\\"\\n        },\\n        {\\n          \\\"class_name\\\": \\\"BinaryAccuracy\\\",\\n          \\\"threshold\\\": {\\n            \\\"change_threshold\\\": {\\n              \\\"absolute\\\": 0.0001,\\n              \\\"direction\\\": \\\"HIGHER_IS_BETTER\\\"\\n            },\\n            \\\"value_threshold\\\": {\\n              \\\"lower_bound\\\": 0.5\\n            }\\n          }\\n        }\\n      ]\\n    }\\n  ],\\n  \\\"model_specs\\\": [\\n    {\\n      \\\"label_key\\\": \\\"stroke\\\"\\n    }\\n  ],\\n  \\\"slicing_specs\\\": [\\n    {},\\n    {\\n      \\\"feature_keys\\\": [\\n        \\\"gender\\\",\\n        \\\"ever_married\\\"\\n      ]\\n    }\\n  ]\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"example_splits\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"fairness_indicator_thresholds\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "upstream_nodes: \"CsvExampleGen\"\n",
            "upstream_nodes: \"Latest_blessed_model_resolver\"\n",
            "upstream_nodes: \"Trainer\"\n",
            "downstream_nodes: \"Pusher\"\n",
            "execution_options {\n",
            "  caching_options {\n",
            "    enable_cache: true\n",
            "  }\n",
            "}\n",
            ", pipeline_info=id: \"stroke-disease-pipeline\"\n",
            ", pipeline_run_id='20230121-083653.609845')\n",
            "INFO:absl:udf_utils.get_fn {'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"metrics\": [\\n        {\\n          \"class_name\": \"AUC\"\\n        },\\n        {\\n          \"class_name\": \"Precision\"\\n        },\\n        {\\n          \"class_name\": \"Recall\"\\n        },\\n        {\\n          \"class_name\": \"ExampleCount\"\\n        },\\n        {\\n          \"class_name\": \"TruePositives\"\\n        },\\n        {\\n          \"class_name\": \"FalsePositives\"\\n        },\\n        {\\n          \"class_name\": \"TrueNegatives\"\\n        },\\n        {\\n          \"class_name\": \"FalseNegatives\"\\n        },\\n        {\\n          \"class_name\": \"BinaryAccuracy\",\\n          \"threshold\": {\\n            \"change_threshold\": {\\n              \"absolute\": 0.0001,\\n              \"direction\": \"HIGHER_IS_BETTER\"\\n            },\\n            \"value_threshold\": {\\n              \"lower_bound\": 0.5\\n            }\\n          }\\n        }\\n      ]\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"stroke\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"gender\",\\n        \"ever_married\"\\n      ]\\n    }\\n  ]\\n}'} 'custom_eval_shared_model'\n",
            "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n",
            "model_specs {\n",
            "  label_key: \"stroke\"\n",
            "}\n",
            "slicing_specs {\n",
            "}\n",
            "slicing_specs {\n",
            "  feature_keys: \"gender\"\n",
            "  feature_keys: \"ever_married\"\n",
            "}\n",
            "metrics_specs {\n",
            "  metrics {\n",
            "    class_name: \"AUC\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Precision\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Recall\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"ExampleCount\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TruePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalsePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TrueNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalseNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"BinaryAccuracy\"\n",
            "    threshold {\n",
            "      value_threshold {\n",
            "        lower_bound {\n",
            "          value: 0.5\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "\n",
            "INFO:absl:Using outputs/stroke-disease-pipeline/Trainer/model/8/Format-Serving as  model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff25c8b8a00> and <keras.engine.input_layer.InputLayer object at 0x7ff25bc6fb50>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff25c8b8a00> and <keras.engine.input_layer.InputLayer object at 0x7ff25bc6fb50>).\n",
            "INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.\n",
            "INFO:absl:Evaluating model.\n",
            "INFO:absl:udf_utils.get_fn {'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"metrics\": [\\n        {\\n          \"class_name\": \"AUC\"\\n        },\\n        {\\n          \"class_name\": \"Precision\"\\n        },\\n        {\\n          \"class_name\": \"Recall\"\\n        },\\n        {\\n          \"class_name\": \"ExampleCount\"\\n        },\\n        {\\n          \"class_name\": \"TruePositives\"\\n        },\\n        {\\n          \"class_name\": \"FalsePositives\"\\n        },\\n        {\\n          \"class_name\": \"TrueNegatives\"\\n        },\\n        {\\n          \"class_name\": \"FalseNegatives\"\\n        },\\n        {\\n          \"class_name\": \"BinaryAccuracy\",\\n          \"threshold\": {\\n            \"change_threshold\": {\\n              \"absolute\": 0.0001,\\n              \"direction\": \"HIGHER_IS_BETTER\"\\n            },\\n            \"value_threshold\": {\\n              \"lower_bound\": 0.5\\n            }\\n          }\\n        }\\n      ]\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"stroke\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"gender\",\\n        \"ever_married\"\\n      ]\\n    }\\n  ]\\n}'} 'custom_extractors'\n",
            "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n",
            "model_specs {\n",
            "  label_key: \"stroke\"\n",
            "}\n",
            "slicing_specs {\n",
            "}\n",
            "slicing_specs {\n",
            "  feature_keys: \"gender\"\n",
            "  feature_keys: \"ever_married\"\n",
            "}\n",
            "metrics_specs {\n",
            "  metrics {\n",
            "    class_name: \"AUC\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Precision\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Recall\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"ExampleCount\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TruePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalsePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TrueNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalseNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"BinaryAccuracy\"\n",
            "    threshold {\n",
            "      value_threshold {\n",
            "        lower_bound {\n",
            "          value: 0.5\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  model_names: \"\"\n",
            "}\n",
            "\n",
            "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n",
            "model_specs {\n",
            "  label_key: \"stroke\"\n",
            "}\n",
            "slicing_specs {\n",
            "}\n",
            "slicing_specs {\n",
            "  feature_keys: \"gender\"\n",
            "  feature_keys: \"ever_married\"\n",
            "}\n",
            "metrics_specs {\n",
            "  metrics {\n",
            "    class_name: \"AUC\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Precision\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Recall\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"ExampleCount\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TruePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalsePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TrueNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalseNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"BinaryAccuracy\"\n",
            "    threshold {\n",
            "      value_threshold {\n",
            "        lower_bound {\n",
            "          value: 0.5\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  model_names: \"\"\n",
            "}\n",
            "\n",
            "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n",
            "model_specs {\n",
            "  label_key: \"stroke\"\n",
            "}\n",
            "slicing_specs {\n",
            "}\n",
            "slicing_specs {\n",
            "  feature_keys: \"gender\"\n",
            "  feature_keys: \"ever_married\"\n",
            "}\n",
            "metrics_specs {\n",
            "  metrics {\n",
            "    class_name: \"AUC\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Precision\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"Recall\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"ExampleCount\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TruePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalsePositives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"TrueNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"FalseNegatives\"\n",
            "  }\n",
            "  metrics {\n",
            "    class_name: \"BinaryAccuracy\"\n",
            "    threshold {\n",
            "      value_threshold {\n",
            "        lower_bound {\n",
            "          value: 0.5\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  model_names: \"\"\n",
            "}\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff25b63a520> and <keras.engine.input_layer.InputLayer object at 0x7ff2569c8df0>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff25b63a520> and <keras.engine.input_layer.InputLayer object at 0x7ff2569c8df0>).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff253ee9640> and <keras.engine.input_layer.InputLayer object at 0x7ff25c757e50>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff253ee9640> and <keras.engine.input_layer.InputLayer object at 0x7ff25c757e50>).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff256d9dc10> and <keras.engine.input_layer.InputLayer object at 0x7ff2543829d0>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff256d9dc10> and <keras.engine.input_layer.InputLayer object at 0x7ff2543829d0>).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff256c3c850> and <keras.engine.input_layer.InputLayer object at 0x7ff25be942e0>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff256c3c850> and <keras.engine.input_layer.InputLayer object at 0x7ff25be942e0>).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff2537bcfd0> and <keras.engine.input_layer.InputLayer object at 0x7ff2537d61f0>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff2537bcfd0> and <keras.engine.input_layer.InputLayer object at 0x7ff2537d61f0>).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff2531dfdc0> and <keras.engine.input_layer.InputLayer object at 0x7ff2531c8250>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff2531dfdc0> and <keras.engine.input_layer.InputLayer object at 0x7ff2531c8250>).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff251a92ee0> and <keras.engine.input_layer.InputLayer object at 0x7ff2531b7d90>).\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. For example, in the saved checkpoint object, `model.layer.weight` and `model.layer_copy.weight` reference the same variable, while in the current object these are two different variables. The referenced variables are:(<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff251a92ee0> and <keras.engine.input_layer.InputLayer object at 0x7ff2531b7d90>).\n",
            "INFO:absl:Evaluation complete. Results written to outputs/stroke-disease-pipeline/Evaluator/evaluation/9.\n",
            "INFO:absl:Checking validation results.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:110: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:110: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n",
            "INFO:absl:Blessing result True written to outputs/stroke-disease-pipeline/Evaluator/blessing/9.\n",
            "INFO:absl:Cleaning up stateless execution info.\n",
            "INFO:absl:Execution 9 succeeded.\n",
            "INFO:absl:Cleaning up stateful execution info.\n",
            "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'blessing': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Evaluator/blessing/9\"\n",
            ", artifact_type: name: \"ModelBlessing\"\n",
            ")], 'evaluation': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Evaluator/evaluation/9\"\n",
            ", artifact_type: name: \"ModelEvaluation\"\n",
            ")]}) for execution 9\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:node Evaluator is finished.\n",
            "INFO:absl:node Pusher is running.\n",
            "INFO:absl:Running launcher for node_info {\n",
            "  type {\n",
            "    name: \"tfx.components.pusher.component.Pusher\"\n",
            "    base_type: DEPLOY\n",
            "  }\n",
            "  id: \"Pusher\"\n",
            "}\n",
            "contexts {\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline_run\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"20230121-083653.609845\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"node\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline.Pusher\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "inputs {\n",
            "  inputs {\n",
            "    key: \"model\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Trainer\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Trainer\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Model\"\n",
            "            base_type: MODEL\n",
            "          }\n",
            "        }\n",
            "        output_key: \"model\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"model_blessing\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Evaluator\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Evaluator\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"ModelBlessing\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"blessing\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "outputs {\n",
            "  outputs {\n",
            "    key: \"pushed_model\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"PushedModel\"\n",
            "          base_type: MODEL\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "parameters {\n",
            "  parameters {\n",
            "    key: \"custom_config\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"push_destination\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"filesystem\\\": {\\n    \\\"base_directory\\\": \\\"outputs/serving_model\\\"\\n  }\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "upstream_nodes: \"Evaluator\"\n",
            "upstream_nodes: \"Trainer\"\n",
            "execution_options {\n",
            "  caching_options {\n",
            "    enable_cache: true\n",
            "  }\n",
            "}\n",
            "\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "WARNING:absl:ArtifactQuery.property_predicate is not supported.\n",
            "INFO:absl:[Pusher] Resolved inputs: ({'model': [Artifact(artifact: id: 15\n",
            "type_id: 30\n",
            "uri: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674293981324\n",
            "last_update_time_since_epoch: 1674293981324\n",
            ", artifact_type: id: 30\n",
            "name: \"Model\"\n",
            "base_type: MODEL\n",
            ")], 'model_blessing': [Artifact(artifact: id: 17\n",
            "type_id: 33\n",
            "uri: \"outputs/stroke-disease-pipeline/Evaluator/blessing/9\"\n",
            "custom_properties {\n",
            "  key: \"blessed\"\n",
            "  value {\n",
            "    int_value: 1\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"current_model\"\n",
            "  value {\n",
            "    string_value: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"current_model_id\"\n",
            "  value {\n",
            "    int_value: 15\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674294000633\n",
            "last_update_time_since_epoch: 1674294000633\n",
            ", artifact_type: id: 33\n",
            "name: \"ModelBlessing\"\n",
            ")]},)\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:Going to run a new execution 10\n",
            "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=10, input_dict={'model': [Artifact(artifact: id: 15\n",
            "type_id: 30\n",
            "uri: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674293981324\n",
            "last_update_time_since_epoch: 1674293981324\n",
            ", artifact_type: id: 30\n",
            "name: \"Model\"\n",
            "base_type: MODEL\n",
            ")], 'model_blessing': [Artifact(artifact: id: 17\n",
            "type_id: 33\n",
            "uri: \"outputs/stroke-disease-pipeline/Evaluator/blessing/9\"\n",
            "custom_properties {\n",
            "  key: \"blessed\"\n",
            "  value {\n",
            "    int_value: 1\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"current_model\"\n",
            "  value {\n",
            "    string_value: \"outputs/stroke-disease-pipeline/Trainer/model/8\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"current_model_id\"\n",
            "  value {\n",
            "    int_value: 15\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"is_external\"\n",
            "  value {\n",
            "    int_value: 0\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"state\"\n",
            "  value {\n",
            "    string_value: \"published\"\n",
            "  }\n",
            "}\n",
            "custom_properties {\n",
            "  key: \"tfx_version\"\n",
            "  value {\n",
            "    string_value: \"1.11.0\"\n",
            "  }\n",
            "}\n",
            "state: LIVE\n",
            "create_time_since_epoch: 1674294000633\n",
            "last_update_time_since_epoch: 1674294000633\n",
            ", artifact_type: id: 33\n",
            "name: \"ModelBlessing\"\n",
            ")]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Pusher/pushed_model/10\"\n",
            ", artifact_type: name: \"PushedModel\"\n",
            "base_type: MODEL\n",
            ")]}), exec_properties={'push_destination': '{\\n  \"filesystem\": {\\n    \"base_directory\": \"outputs/serving_model\"\\n  }\\n}', 'custom_config': 'null'}, execution_output_uri='outputs/stroke-disease-pipeline/Pusher/.system/executor_execution/10/executor_output.pb', stateful_working_dir='outputs/stroke-disease-pipeline/Pusher/.system/stateful_working_dir/20230121-083653.609845', tmp_dir='outputs/stroke-disease-pipeline/Pusher/.system/executor_execution/10/.temp/', pipeline_node=node_info {\n",
            "  type {\n",
            "    name: \"tfx.components.pusher.component.Pusher\"\n",
            "    base_type: DEPLOY\n",
            "  }\n",
            "  id: \"Pusher\"\n",
            "}\n",
            "contexts {\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"pipeline_run\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"20230121-083653.609845\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  contexts {\n",
            "    type {\n",
            "      name: \"node\"\n",
            "    }\n",
            "    name {\n",
            "      field_value {\n",
            "        string_value: \"stroke-disease-pipeline.Pusher\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "inputs {\n",
            "  inputs {\n",
            "    key: \"model\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Trainer\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Trainer\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"Model\"\n",
            "            base_type: MODEL\n",
            "          }\n",
            "        }\n",
            "        output_key: \"model\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  inputs {\n",
            "    key: \"model_blessing\"\n",
            "    value {\n",
            "      channels {\n",
            "        producer_node_query {\n",
            "          id: \"Evaluator\"\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"pipeline_run\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"20230121-083653.609845\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        context_queries {\n",
            "          type {\n",
            "            name: \"node\"\n",
            "          }\n",
            "          name {\n",
            "            field_value {\n",
            "              string_value: \"stroke-disease-pipeline.Evaluator\"\n",
            "            }\n",
            "          }\n",
            "        }\n",
            "        artifact_query {\n",
            "          type {\n",
            "            name: \"ModelBlessing\"\n",
            "          }\n",
            "        }\n",
            "        output_key: \"blessing\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "outputs {\n",
            "  outputs {\n",
            "    key: \"pushed_model\"\n",
            "    value {\n",
            "      artifact_spec {\n",
            "        type {\n",
            "          name: \"PushedModel\"\n",
            "          base_type: MODEL\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "parameters {\n",
            "  parameters {\n",
            "    key: \"custom_config\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"null\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "  parameters {\n",
            "    key: \"push_destination\"\n",
            "    value {\n",
            "      field_value {\n",
            "        string_value: \"{\\n  \\\"filesystem\\\": {\\n    \\\"base_directory\\\": \\\"outputs/serving_model\\\"\\n  }\\n}\"\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "upstream_nodes: \"Evaluator\"\n",
            "upstream_nodes: \"Trainer\"\n",
            "execution_options {\n",
            "  caching_options {\n",
            "    enable_cache: true\n",
            "  }\n",
            "}\n",
            ", pipeline_info=id: \"stroke-disease-pipeline\"\n",
            ", pipeline_run_id='20230121-083653.609845')\n",
            "INFO:absl:Model version: 1674294000\n",
            "INFO:absl:Model written to serving path outputs/serving_model/1674294000.\n",
            "INFO:absl:Model pushed to outputs/stroke-disease-pipeline/Pusher/pushed_model/10.\n",
            "INFO:absl:Cleaning up stateless execution info.\n",
            "INFO:absl:Execution 10 succeeded.\n",
            "INFO:absl:Cleaning up stateful execution info.\n",
            "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: \"outputs/stroke-disease-pipeline/Pusher/pushed_model/10\"\n",
            ", artifact_type: name: \"PushedModel\"\n",
            "base_type: MODEL\n",
            ")]}) for execution 10\n",
            "INFO:absl:MetadataStore with DB connection initialized\n",
            "INFO:absl:node Pusher is finished.\n"
          ]
        }
      ],
      "source": [
        "logging.set_verbosity(logging.INFO)\n",
        "\n",
        "components = components.init_components({\n",
        "    'data_dir': DATA_ROOT,\n",
        "    'transform_module': TRANSFORM_MODULE_FILE,\n",
        "    'tuner_module': TUNER_MODULE_FILE,\n",
        "    'training_module': TRAINER_MODULE_FILE,\n",
        "    'training_steps': 5000,\n",
        "    'eval_steps': 1000,\n",
        "    'serving_model_dir': serving_model_dir\n",
        "})\n",
        "\n",
        "pipeline = init_local_pipeline(components, pipeline_root)\n",
        "BeamDagRunner().run(pipeline=pipeline)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KlKcnFRwbNU",
        "outputId": "2119eeb9-ab5d-47ac-da1f-0eab8b21e211"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  adding: data/ (stored 0%)\n",
            "  adding: data/healthcare-dataset-stroke-data.csv (deflated 83%)\n",
            "  adding: images/ (stored 0%)\n",
            "  adding: images/model_plot.png (deflated 20%)\n",
            "  adding: outputs/ (stored 0%)\n",
            "  adding: outputs/serving_model/ (stored 0%)\n",
            "  adding: outputs/serving_model/1674294000/ (stored 0%)\n",
            "  adding: outputs/serving_model/1674294000/saved_model.pb (deflated 89%)\n",
            "  adding: outputs/serving_model/1674294000/assets/ (stored 0%)\n",
            "  adding: outputs/serving_model/1674294000/assets/vocab_compute_and_apply_vocabulary_1_vocabulary (stored 0%)\n",
            "  adding: outputs/serving_model/1674294000/assets/vocab_compute_and_apply_vocabulary_4_vocabulary (deflated 18%)\n",
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            "  adding: outputs/serving_model/1674294000/keras_metadata.pb (deflated 91%)\n",
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            "  adding: outputs/stroke-disease-pipeline/_wheels/ (stored 0%)\n",
            "  adding: outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Tuner-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl (deflated 13%)\n",
            "  adding: outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Trainer-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl (deflated 14%)\n",
            "  adding: outputs/stroke-disease-pipeline/_wheels/tfx_user_code_Transform-0.0+420d0b25a87e97753c5601655e878671b6d8a2d3908e307f81d59c59e6f5f087-py3-none-any.whl (deflated 14%)\n",
            "  adding: outputs/stroke-disease-pipeline/Pusher/ (stored 0%)\n",
            "  adding: outputs/stroke-disease-pipeline/Pusher/pushed_model/ (stored 0%)\n",
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            "  adding: outputs/stroke-disease-pipeline/Pusher/pushed_model/10/variables/variables.data-00000-of-00001 (deflated 25%)\n",
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            "  adding: outputs/stroke-disease-pipeline/CsvExampleGen/ (stored 0%)\n",
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            "  adding: outputs/stroke-disease-pipeline/CsvExampleGen/examples/1/ (stored 0%)\n",
            "  adding: outputs/stroke-disease-pipeline/CsvExampleGen/examples/1/Split-train/ (stored 0%)\n",
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            "  adding: outputs/stroke-disease-pipeline/ExampleValidator/.system/executor_execution/5/ (stored 0%)\n"
          ]
        }
      ],
      "source": [
        "!zip -r data.zip data/\n",
        "!zip -r images.zip images/\n",
        "!zip -r outputs.zip outputs/\n",
        "!pip freeze > requirements.txt"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}