Machine Learning Pipeline with TFX.ipynb

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    "#Installation of library\n",
    "# !pip install -q -U tfx==1.4.0\n",
    "# !pip install tensorflow==2.7.0\n",
    "# !pip install tensorflow_decision_forests==0.2.1"
   ]
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   "source": [
    "# creating required directories\n",
    "# !mkdir -p saved_data\n",
    "# !mkdir -p model\n",
    "# !mkdir -p data"
   ]
  },
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     "text": [
      "WARNING:root:Failure to load the custom c++ tensorflow ops. This error is likely caused the version of TensorFlow and TensorFlow Decision Forests are not compatible.\n",
      "WARNING:root:TF Parameter Server distributed training not available.\n"
     ]
    },
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     "text": [
      "Tensorflow Version: 2.7.0\n",
      "TFX Version: 1.4.0\n",
      "TFDF Version: 0.2.1\n",
      "Tensorflow Data Validation Version: 1.4.0\n"
     ]
    }
   ],
   "source": [
    "#importing packages\n",
    "import tempfile\n",
    "import tensorflow as tf\n",
    "import urllib.request\n",
    "import os\n",
    "import pandas as pd\n",
    "import shutil\n",
    "import tensorflow_data_validation as tfdv\n",
    "import tensorflow_model_analysis as tfma\n",
    "import tensorflow_decision_forests as tfdf\n",
    "\n",
    "from absl import logging\n",
    "from pathlib import Path\n",
    "from tfx import v1 as tfx\n",
    "from tensorflow_metadata.proto.v0 import schema_pb2\n",
    "from tfx.proto import example_gen_pb2\n",
    "\n",
    "print(f\"Tensorflow Version: {tf.__version__}\")\n",
    "print(f\"TFX Version: {tfx.__version__}\")\n",
    "print(f\"TFDF Version: {tfdf.__version__}\")\n",
    "\n",
    "print(f\"Tensorflow Data Validation Version: {tfdv.__version__}\")\n",
    "\n",
    "logging.set_verbosity(logging.INFO)"
   ]
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   "source": [
    "DATA_DIRECTORY = 'data'\n",
    "DATA_SOURCE_PATH = Path(DATA_DIRECTORY) / 'Social_Network_Ads.csv'\n",
    "\n",
    "SAVED_DATA = 'saved_data'\n",
    "DATA_TRAIN_FILENAME = Path(SAVED_DATA) / 'train.csv'\n",
    "DATA_TEST_FILENAME = Path(SAVED_DATA) / 'test.csv'\n",
    "\n",
    "PIPELINE_NAME = 'sample-pipeline'\n",
    "PIPELINE_DIRECTORY = os.path.join(Path('pipelines'), PIPELINE_NAME)\n",
    "\n",
    "METADATA_PATH = Path(\"metadata\") / PIPELINE_NAME / \"metadata.db\"\n",
    "\n",
    "SCHEMA_DIRECTORY = os.path.join(PIPELINE_DIRECTORY, 'schema')\n",
    "SCHEMA_FILE_NAME = str(os.path.join(SCHEMA_DIRECTORY, 'schema.pbtxt'))\n",
    "\n",
    "MODEL_DIRECTORY = Path('model')\n",
    "\n",
    "# Module Paths\n",
    "CONSTANTS_MODULE_PATH = 'constants.py'\n",
    "TRANSFORM_MODULE_PATH = 'transform.py'\n",
    "TRAINER_MODULE_PATH = 'trainer.py'"
   ]
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     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
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   "source": [
    "#Reading files\n",
    "data_df = pd.read_csv(DATA_SOURCE_PATH)\n",
    "\n",
    "# splitting the data for training and testing\n",
    "data_df = data_df.sample(frac=1)\n",
    "train_df = data_df[: int(len(data_df) * 0.7)]\n",
    "test_df = data_df[int(len(data_df) * 0.7): ]\n",
    "\n",
    "# removing the undesired columns from all the datasets\n",
    "datasets = [train_df, test_df]\n",
    "drop_columns = ['User ID']\n",
    "for dataset in datasets:\n",
    "    dataset.drop(drop_columns, axis=1, inplace=True)\n",
    "\n",
    "train_df.to_csv(DATA_TRAIN_FILENAME, index=False)\n",
    "test_df.to_csv(DATA_TEST_FILENAME, index=False)"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 280 entries, 212 to 153\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Gender           280 non-null    object\n",
      " 1   Age              280 non-null    int64 \n",
      " 2   EstimatedSalary  280 non-null    int64 \n",
      " 3   Purchased        280 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 10.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# peeking at the data\n",
    "train_df.info()"
   ]
  },
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   "source": [
    "##Common Functions\n",
    "# creating the useful conversion functions\n",
    "\n",
    "def _bytes_feature(value):\n",
    "    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(str(value), encoding=\"raw_unicode_escape\")]))\n",
    "\n",
    "def _float_feature(value):\n",
    "    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n",
    "\n",
    "def _int64_feature(value):\n",
    "    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e58f53f-1f7b-4650-8286-9e65cb706045",
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   "source": [
    "# This function will be required to convert out test set \n",
    "# to compatible schema types for inferencing\n",
    "def _examples(df):\n",
    "    examples = []\n",
    "    for index, row in df.iterrows():\n",
    "        features = {\n",
    "            \"Gender\": _bytes_feature(row['Gender']),\n",
    "            \"Age\": _int64_feature(row['Age']),\n",
    "            \"EstimatedSalary\": _int64_feature(row['EstimatedSalary']),\n",
    "        }\n",
    "        example_proto = tf.train.Example(features=tf.train.Features(feature=features))\n",
    "        examples.append(example_proto.SerializeToString())\n",
    "\n",
    "    return examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eac0a9e-86f5-445c-ad2d-9dd0b5fd347f",
   "metadata": {},
   "source": [
    "### Running components interactively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1b3c831-619c-412d-9fdf-756cb3aef990",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1 as root for pipeline outputs.\n",
      "WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/metadata.sqlite.\n"
     ]
    }
   ],
   "source": [
    "from tfx.orchestration.experimental.interactive.interactive_context import (InteractiveContext)\n",
    "\n",
    "context = InteractiveContext()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652ec640-25ce-4105-97a1-91429ca17d99",
   "metadata": {},
   "source": [
    "### Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca7e8ec7-dfaa-471d-bbd1-0db7bbb5c167",
   "metadata": {},
   "outputs": [
    {
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      "INFO:absl:Running driver for CsvExampleGen\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:select span and version = (0, None)\n",
      "INFO:absl:latest span and version = (0, None)\n",
      "INFO:absl:Running executor for CsvExampleGen\n",
      "INFO:absl:Generating examples.\n",
      "WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.\n"
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      "INFO:absl:Processing input csv data saved_data/* to TFExample.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n",
      "WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\n",
      "INFO:absl:Examples generated.\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7fa91fa805d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.exec_properties</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['input_base']</td><td class = \"attrvalue\">saved_data</td></tr><tr><td class=\"attr-name\">['input_config']</td><td class = \"attrvalue\">{\n",
       "  &quot;splits&quot;: [\n",
       "    {\n",
       "      &quot;name&quot;: &quot;single_split&quot;,\n",
       "      &quot;pattern&quot;: &quot;*&quot;\n",
       "    }\n",
       "  ]\n",
       "}</td></tr><tr><td class=\"attr-name\">['output_config']</td><td class = \"attrvalue\">{\n",
       "  &quot;split_config&quot;: {\n",
       "    &quot;splits&quot;: [\n",
       "      {\n",
       "        &quot;hash_buckets&quot;: 3,\n",
       "        &quot;name&quot;: &quot;train&quot;\n",
       "      },\n",
       "      {\n",
       "        &quot;hash_buckets&quot;: 1,\n",
       "        &quot;name&quot;: &quot;eval&quot;\n",
       "      }\n",
       "    ]\n",
       "  }\n",
       "}</td></tr><tr><td class=\"attr-name\">['output_data_format']</td><td class = \"attrvalue\">6</td></tr><tr><td class=\"attr-name\">['output_file_format']</td><td class = \"attrvalue\">5</td></tr><tr><td class=\"attr-name\">['custom_config']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['range_config']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['span']</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">['version']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['input_fingerprint']</td><td class = \"attrvalue\">split:single_split,num_files:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042</td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">.component.inputs</td><td class = \"attrvalue\">{}</td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['examples']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       "  border: 1px solid #bbbbbb;\n",
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       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91f6b9a10</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Examples</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7fa91fa805d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: CsvExampleGen\n",
       "    execution_id: 1\n",
       "    outputs:\n",
       "        examples: Channel(\n",
       "            type_name: Examples\n",
       "            artifacts: [Artifact(artifact: id: 1\n",
       "        type_id: 14\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/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:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 14\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",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = tfx.proto.Output(\n",
    "    split_config=example_gen_pb2.SplitConfig(splits=[\n",
    "                                                     tfx.proto.SplitConfig.Split(name=\"train\", hash_buckets=3),\n",
    "                                                     tfx.proto.SplitConfig.Split(name=\"eval\", hash_buckets=1)\n",
    "    ]))\n",
    "example_gen = tfx.components.CsvExampleGen(input_base=SAVED_DATA, output_config=output)\n",
    "\n",
    "context.run(example_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf67d5e7-3598-49c5-aa79-ab0fc5dd60d5",
   "metadata": {},
   "source": [
    "### Generating Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17a7bd89-32bf-4a69-ae4e-5ee1212a1365",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Excluding no splits because exclude_splits is not set.\n",
      "INFO:absl:Running driver for StatisticsGen\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Running executor for StatisticsGen\n",
      "INFO:absl:Generating statistics for split train.\n",
      "INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/StatisticsGen/statistics/2/Split-train.\n",
      "INFO:absl:Generating statistics for split eval.\n",
      "INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/StatisticsGen/statistics/2/Split-eval.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n",
      "INFO:absl:Running publisher for StatisticsGen\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>Artifact at /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/StatisticsGen/statistics/2</b><br/><br/>"
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<div><b>'train' split:</b></div><br/>"
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     "output_type": "display_data"
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       "        facets_iframe.srcdoc = facets_html;\n",
       "         facets_iframe.id = \"\";\n",
       "         setTimeout(() => {\n",
       "           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
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       "         </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "statistics_gen = tfx.components.StatisticsGen(\n",
    "    examples=example_gen.outputs['examples']\n",
    ")\n",
    "context.run(statistics_gen)\n",
    "context.show(statistics_gen.outputs['statistics'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bbc1408-b811-4345-8e95-cb6abbded6d0",
   "metadata": {},
   "source": [
    "### Generating Data Schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc727b02-047e-4fce-9a4d-b4bd447504a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Excluding no splits because exclude_splits is not set.\n",
      "INFO:absl:Running driver for SchemaGen\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "WARNING: Logging before InitGoogleLogging() is written to STDERR\n",
      "I0312 05:35:24.499311  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Running executor for SchemaGen\n",
      "INFO:absl:Processing schema from statistics for split train.\n",
      "INFO:absl:Processing schema from statistics for split eval.\n",
      "INFO:absl:Schema written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/SchemaGen/schema/3/schema.pbtxt.\n",
      "INFO:absl:Running publisher for SchemaGen\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>Artifact at /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/SchemaGen/schema/3</b><br/><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Presence</th>\n",
       "      <th>Valency</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Gender'</th>\n",
       "      <td>STRING</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>'Gender'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Age'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'EstimatedSalary'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Purchased'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Type  Presence Valency    Domain\n",
       "Feature name                                         \n",
       "'Gender'           STRING  required          'Gender'\n",
       "'Age'                 INT  required                 -\n",
       "'EstimatedSalary'     INT  required                 -\n",
       "'Purchased'           INT  required                 -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domain</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Gender'</th>\n",
       "      <td>'Female', 'Male'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Values\n",
       "Domain                    \n",
       "'Gender'  'Female', 'Male'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schema_gen = tfx.components.SchemaGen(\n",
    "    statistics=statistics_gen.outputs['statistics'],\n",
    "    infer_feature_shape=True\n",
    ")\n",
    "context.run(schema_gen)\n",
    "context.show(schema_gen.outputs['schema'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962d5f82-1e87-4374-900e-482d14253dcd",
   "metadata": {},
   "source": [
    "### Updating the schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a479e68-eb90-41f7-826c-6a28d4944b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Presence</th>\n",
       "      <th>Valency</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Gender'</th>\n",
       "      <td>STRING</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>'Gender'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Age'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'EstimatedSalary'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Purchased'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Type  Presence Valency    Domain\n",
       "Feature name                                         \n",
       "'Gender'           STRING  required          'Gender'\n",
       "'Age'                 INT  required                 -\n",
       "'EstimatedSalary'     INT  required                 -\n",
       "'Purchased'           INT  required                 -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domain</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Gender'</th>\n",
       "      <td>'Female', 'Male'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Values\n",
       "Domain                    \n",
       "'Gender'  'Female', 'Male'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schema = tfdv.load_schema_text(\n",
    "    os.path.join(schema_gen.outputs['schema']._artifacts[0].uri, \"schema.pbtxt\")\n",
    ")\n",
    "\n",
    "# adding the needed environments\n",
    "schema.default_environment.append(\"TRAINING\")\n",
    "schema.default_environment.append(\"SERVING\")\n",
    "\n",
    "# removing the `Purchased` column from the serving environment\n",
    "tfdv.get_feature(schema, \"Purchased\").not_in_environment.append(\"SERVING\")\n",
    "\n",
    "tfdv.display_schema(schema=schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12e0862b-6ca7-46ec-ae38-1a1659aeafe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Running driver for schema_importer\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Processing source uri: pipelines/sample-pipeline/schema, properties: {}, custom_properties: {}\n",
      "INFO:absl:Running executor for schema_importer\n",
      "INFO:absl:Running publisher for schema_importer\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>Artifact at pipelines/sample-pipeline/schema</b><br/><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Presence</th>\n",
       "      <th>Valency</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Gender'</th>\n",
       "      <td>STRING</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>'Gender'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Age'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'EstimatedSalary'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Purchased'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Type  Presence Valency    Domain\n",
       "Feature name                                         \n",
       "'Gender'           STRING  required          'Gender'\n",
       "'Age'                 INT  required                 -\n",
       "'EstimatedSalary'     INT  required                 -\n",
       "'Purchased'           INT  required                 -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domain</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Gender'</th>\n",
       "      <td>'Female', 'Male'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Values\n",
       "Domain                    \n",
       "'Gender'  'Female', 'Male'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!mkdir -p {SCHEMA_DIRECTORY}\n",
    "\n",
    "tfdv.write_schema_text(schema, SCHEMA_FILE_NAME)\n",
    "\n",
    "# loading the updated schema using the importer node.\n",
    "schema_importer = tfx.dsl.Importer(\n",
    "    source_uri=str(SCHEMA_DIRECTORY),\n",
    "    artifact_type=tfx.types.standard_artifacts.Schema\n",
    ").with_id(\"schema_importer\")\n",
    "\n",
    "context.run(schema_importer)\n",
    "context.show(schema_importer.outputs['result'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2e1031-ed31-4a4b-98bc-a455375f89ee",
   "metadata": {},
   "source": [
    "### Data Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25e2ac5b-0710-4203-b8c2-3747cb04f551",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Excluding no splits because exclude_splits is not set.\n",
      "INFO:absl:Running driver for ExampleValidator\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Running executor for ExampleValidator\n",
      "INFO:absl:Validating schema against the computed statistics for split train.\n",
      "INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/ExampleValidator/anomalies/5/Split-train.\n",
      "INFO:absl:Validating schema against the computed statistics for split eval.\n",
      "INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/ExampleValidator/anomalies/5/Split-eval.\n",
      "INFO:absl:Running publisher for ExampleValidator\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>Artifact at /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/ExampleValidator/anomalies/5</b><br/><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><b>'train' split:</b></div><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4 style=\"color:green;\">No anomalies found.</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><b>'eval' split:</b></div><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4 style=\"color:green;\">No anomalies found.</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_validator = tfx.components.ExampleValidator(\n",
    "    statistics=statistics_gen.outputs['statistics'],\n",
    "    schema=schema_importer.outputs['result']\n",
    ")\n",
    "\n",
    "context.run(example_validator)\n",
    "context.show(example_validator.outputs['anomalies'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bac841-8e44-4995-825b-f18298772765",
   "metadata": {},
   "source": [
    "### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "215dbc59-a1a8-49aa-9335-e1648da0dea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing constants.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile {CONSTANTS_MODULE_PATH}\n",
    "LABEL = 'Purchased'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88c116a0-0047-475d-8a11-8884ab65ec96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing transform.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile {TRANSFORM_MODULE_PATH}\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_transform as tft\n",
    "\n",
    "import constants\n",
    "\n",
    "LABEL = constants.LABEL\n",
    "\n",
    "def preprocessing_fn(inputs):\n",
    "\n",
    "    outputs = dict()\n",
    "    outputs['Age'] = inputs['Age']\n",
    "    outputs['EstimatedSalary'] = inputs['EstimatedSalary']\n",
    "    # converting the `Gender` into label encoded column.\n",
    "    outputs['Gender'] = tf.cast(tf.equal(inputs['Gender'], 'male'), tf.int64)\n",
    "\n",
    "    outputs[LABEL] = inputs[LABEL]\n",
    "\n",
    "    return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23695ba3-7593-4a0e-bdd0-cdd40a653ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Generating ephemeral wheel package for '/home/jupyter/MLOps_Pipeline_project/transform.py' (including modules: ['constants', 'transform']).\n",
      "INFO:absl:User module package has hash fingerprint version e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '/tmp/tmppzyjecgv/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp_w_lno38', '--dist-dir', '/tmp/tmplnxl1xj4']\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
      "  setuptools.SetuptoolsDeprecationWarning,\n",
      "INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl'; target user module is 'transform'.\n",
      "INFO:absl:Full user module path is 'transform@/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl'\n",
      "INFO:absl:Running driver for Transform\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:41:27.270299  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:41:27.276913  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Running executor for Transform\n",
      "INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.\n",
      "INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'transform@/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'\n",
      "INFO:absl:Installing '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpzar1lyk_', '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running bdist_wheel\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib\n",
      "copying constants.py -> build/lib\n",
      "copying transform.py -> build/lib\n",
      "installing to /tmp/tmp_w_lno38\n",
      "running install\n",
      "running install_lib\n",
      "copying build/lib/constants.py -> /tmp/tmp_w_lno38\n",
      "copying build/lib/transform.py -> /tmp/tmp_w_lno38\n",
      "running install_egg_info\n",
      "running egg_info\n",
      "creating tfx_user_code_Transform.egg-info\n",
      "writing tfx_user_code_Transform.egg-info/PKG-INFO\n",
      "writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt\n",
      "writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt\n",
      "writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n",
      "reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n",
      "writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n",
      "Copying tfx_user_code_Transform.egg-info to /tmp/tmp_w_lno38/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3.7.egg-info\n",
      "running install_scripts\n",
      "creating /tmp/tmp_w_lno38/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa.dist-info/WHEEL\n",
      "creating '/tmp/tmplnxl1xj4/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl' and adding '/tmp/tmp_w_lno38' to it\n",
      "adding 'constants.py'\n",
      "adding 'transform.py'\n",
      "adding 'tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa.dist-info/METADATA'\n",
      "adding 'tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa.dist-info/WHEEL'\n",
      "adding 'tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa.dist-info/top_level.txt'\n",
      "adding 'tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa.dist-info/RECORD'\n",
      "removing /tmp/tmp_w_lno38\n",
      "Processing /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl'.\n",
      "INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'transform@/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'\n",
      "INFO:absl:Installing '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp67zoq1lh', '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Transform\n",
      "Successfully installed tfx-user-code-Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa\n",
      "Processing /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl'.\n",
      "INFO:absl:Installing '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpduhkaif4', '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Transform\n",
      "Successfully installed tfx-user-code-Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa\n",
      "Processing /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa-py3-none-any.whl'.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Transform\n",
      "Successfully installed tfx-user-code-Transform-0.0+e91a37d91407d0ae52616ab9833d7bc7403f4f6ac14dccd81c95c85c68fe17fa\n",
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use ref() instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-12 05:41:37.159240: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n",
      "2022-03-12 05:41:37.159358: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-03-12 05:41:37.159387: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tensorflow-2-6-20220309-131623): /proc/driver/nvidia/version does not exist\n",
      "2022-03-12 05:41:37.159876: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use ref() instead.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n",
      "2022-03-12 05:41:40.671143: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6/.temp_path/tftransform_tmp/7d5c524518d546569a7328e3b0d55d21/assets\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6/.temp_path/tftransform_tmp/7d5c524518d546569a7328e3b0d55d21/assets\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: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_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:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n",
      "INFO:absl:Running publisher for Transform\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
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       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'TransformGraph'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae6450</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">TransformGraph</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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       "</style>\n",
       "<script>\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae65d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Examples</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transformed_examples/6)<span class=\"deemphasize\"> at 0x7fa951103150</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transformed_examples/6</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['updated_analyzer_cache']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object, .tfx-object * {\n",
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       "</style>\n",
       "<script>\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'TransformCache'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bca823d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">TransformCache</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'TransformCache'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/updated_analyzer_cache/6)<span class=\"deemphasize\"> at 0x7fa9bca82650</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.TransformCache&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/updated_analyzer_cache/6</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['pre_transform_schema']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object table.attr-table {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/pre_transform_schema/6)<span class=\"deemphasize\"> at 0x7fa9bca82d50</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Schema&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/pre_transform_schema/6</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['pre_transform_stats']</td><td class = \"attrvalue\"><style>\n",
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       ".tfx-object table.attr-table {\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/pre_transform_stats/6)<span class=\"deemphasize\"> at 0x7fa9bca82f90</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/pre_transform_stats/6</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\"></td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['post_transform_schema']</td><td class = \"attrvalue\"><style>\n",
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       "<script>\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "<script>\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_stats/6)<span class=\"deemphasize\"> at 0x7fa9bca82410</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_stats/6</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\"></td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['post_transform_anomalies']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       "<script>\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae6f90</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Schema</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_schema/6)<span class=\"deemphasize\"> at 0x7fa9bca82950</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Schema&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_schema/6</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['post_transform_stats']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae6490</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleStatistics</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_stats/6)<span class=\"deemphasize\"> at 0x7fa9bca82410</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_stats/6</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\"></td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['post_transform_anomalies']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae6e50</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleAnomalies</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_anomalies/6)<span class=\"deemphasize\"> at 0x7fa9bca82250</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleAnomalies&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_anomalies/6</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\"></td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: Transform\n",
       "    execution_id: 6\n",
       "    outputs:\n",
       "        transform_graph: Channel(\n",
       "            type_name: TransformGraph\n",
       "            artifacts: [Artifact(artifact: id: 6\n",
       "        type_id: 23\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"transform_graph\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 23\n",
       "        name: \"TransformGraph\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        transformed_examples: Channel(\n",
       "            type_name: Examples\n",
       "            artifacts: [Artifact(artifact: id: 7\n",
       "        type_id: 14\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transformed_examples/6\"\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value {\n",
       "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"transformed_examples\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 14\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",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        updated_analyzer_cache: Channel(\n",
       "            type_name: TransformCache\n",
       "            artifacts: [Artifact(artifact: id: 8\n",
       "        type_id: 24\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/updated_analyzer_cache/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"updated_analyzer_cache\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 24\n",
       "        name: \"TransformCache\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        pre_transform_schema: Channel(\n",
       "            type_name: Schema\n",
       "            artifacts: [Artifact(artifact: id: 9\n",
       "        type_id: 18\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/pre_transform_schema/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"pre_transform_schema\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 18\n",
       "        name: \"Schema\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        pre_transform_stats: Channel(\n",
       "            type_name: ExampleStatistics\n",
       "            artifacts: [Artifact(artifact: id: 10\n",
       "        type_id: 16\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/pre_transform_stats/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"pre_transform_stats\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 16\n",
       "        name: \"ExampleStatistics\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        post_transform_schema: Channel(\n",
       "            type_name: Schema\n",
       "            artifacts: [Artifact(artifact: id: 11\n",
       "        type_id: 18\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_schema/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"post_transform_schema\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 18\n",
       "        name: \"Schema\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        post_transform_stats: Channel(\n",
       "            type_name: ExampleStatistics\n",
       "            artifacts: [Artifact(artifact: id: 12\n",
       "        type_id: 16\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_stats/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"post_transform_stats\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 16\n",
       "        name: \"ExampleStatistics\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        post_transform_anomalies: Channel(\n",
       "            type_name: ExampleAnomalies\n",
       "            artifacts: [Artifact(artifact: id: 13\n",
       "        type_id: 21\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/post_transform_anomalies/6\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"post_transform_anomalies\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 21\n",
       "        name: \"ExampleAnomalies\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform = tfx.components.Transform(\n",
    "    examples=example_gen.outputs['examples'],\n",
    "    schema=schema_importer.outputs['result'],\n",
    "    module_file=os.path.abspath(TRANSFORM_MODULE_PATH),\n",
    ")\n",
    "context.run(transform, enable_cache=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c51302c-ed36-44b2-99e0-f71905bc8d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features {\n",
      "  feature {\n",
      "    key: \"Age\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 59\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"EstimatedSalary\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 42000\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"Gender\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"Purchased\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "features {\n",
      "  feature {\n",
      "    key: \"Age\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 47\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"EstimatedSalary\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 25000\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"Gender\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"Purchased\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 1\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "features {\n",
      "  feature {\n",
      "    key: \"Age\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 26\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"EstimatedSalary\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 35000\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"Gender\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"Purchased\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#We can take a look at the transformed examples from our data.\n",
    "train_uri = os.path.join(\n",
    "    transform.outputs['transformed_examples'].get()[0].uri,\n",
    "    'Split-train'\n",
    ")\n",
    "\n",
    "tfrecord_filenames = [\n",
    "                      os.path.join(train_uri, name) for name in os.listdir(train_uri)\n",
    "]\n",
    "\n",
    "dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type='GZIP')\n",
    "\n",
    "for tfrecord in dataset.take(3):\n",
    "    serialized_example = tfrecord.numpy()\n",
    "    example = tf.train.Example()\n",
    "    example.ParseFromString(serialized_example)\n",
    "    print(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b44f988-74c8-4933-a261-74c6074db119",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37720e44-f5a1-40a5-b76d-d2f22be9348b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing trainer.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile {TRAINER_MODULE_PATH}\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_decision_forests as tfdf\n",
    "import tensorflow_transform as tft\n",
    "\n",
    "from absl import logging\n",
    "from tensorflow.keras import layers, Model, optimizers, losses, metrics\n",
    "from tfx import v1 as tfx\n",
    "from tfx_bsl.public import tfxio\n",
    "from typing import List, Text\n",
    "\n",
    "import constants\n",
    "\n",
    "LABEL = constants.LABEL\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 50\n",
    "\n",
    "def _input_fn(\n",
    "    file_pattern: List[Text],\n",
    "    data_accessor: tfx.components.DataAccessor,\n",
    "    tf_transform_output: tft.TFTransformOutput,\n",
    "    batch_size: int,\n",
    ") -> tf.data.Dataset:\n",
    "\n",
    "    \"\"\"\n",
    "    Generates a dataset of features that can be used to train\n",
    "    and evaluate the model.\n",
    "\n",
    "    Args:\n",
    "        file_pattern: List of paths or patterns of input data files.\n",
    "        data_accessor: An instance of DataAccessor that we can use to\n",
    "            convert the input to a RecordBatch.\n",
    "        tf_transform_output: The transformation output.\n",
    "        batch_size: The number of consecutive elements that we should\n",
    "            combine in a single batch.\n",
    "\n",
    "    Returns:\n",
    "        A dataset that contains a tuple of (features, indices) where \n",
    "            features is a dictionary of Tensors, and indices is a single\n",
    "            Tensor of label indices.\n",
    "    \"\"\"\n",
    "\n",
    "    dataset = data_accessor.tf_dataset_factory(\n",
    "        file_pattern,\n",
    "        tfxio.TensorFlowDatasetOptions(batch_size=batch_size),\n",
    "        schema=tf_transform_output.raw_metadata.schema,\n",
    "    )\n",
    "\n",
    "    tft_layer = tf_transform_output.transform_features_layer()\n",
    "\n",
    "    def apply_transform(raw_features):\n",
    "        transformed_features = tft_layer(raw_features)\n",
    "        transformed_label = transformed_features.pop(LABEL)\n",
    "        return transformed_features, transformed_label\n",
    "    \n",
    "    return dataset.map(apply_transform).repeat()\n",
    "\n",
    "def _get_serve_tf_examples_fn(model, tf_transform_output):\n",
    "    \"\"\"\n",
    "    Returns a function that parses a serialized tf.Example and applies\n",
    "    the transformations during inference.\n",
    "    Args:\n",
    "        model: The model that we are serving.\n",
    "        tf_transform_output: The transformation output that we want to \n",
    "            include with the model.\n",
    "    \"\"\"\n",
    "    \n",
    "    model.tft_layer = tf_transform_output.transform_features_layer()\n",
    "\n",
    "    @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name=\"examples\")])\n",
    "    def serve_tf_examples_fn(serialized_tf_examples):\n",
    "        feature_spec = tf_transform_output.raw_feature_spec()\n",
    "        \n",
    "        required_feature_spec = {\n",
    "            k: v for k, v in feature_spec.items() if k != LABEL\n",
    "        }\n",
    "\n",
    "        parsed_features = tf.io.parse_example(\n",
    "            serialized_tf_examples,\n",
    "            required_feature_spec\n",
    "        )\n",
    "\n",
    "        transformed_features = model.tft_layer(parsed_features)\n",
    "\n",
    "        return model(transformed_features)\n",
    "\n",
    "    return serve_tf_examples_fn\n",
    "\n",
    "def _model() -> tf.keras.Model:\n",
    "    inputs = [\n",
    "              layers.Input(shape=(1,), name=\"Age\"),\n",
    "              layers.Input(shape=(1,), name=\"EstimatedSalary\"),\n",
    "              layers.Input(shape=(1,), name=\"Gender\")\n",
    "    ]\n",
    "\n",
    "    x = layers.concatenate(inputs)\n",
    "    x = layers.Dense(8, activation=\"relu\")(x)\n",
    "    x = layers.Dense(8, activation=\"relu\")(x)\n",
    "\n",
    "    outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "    model.compile(\n",
    "        optimizer=optimizers.Adam(1e-2),\n",
    "        loss=\"binary_crossentropy\",\n",
    "        metrics=[metrics.BinaryAccuracy()],\n",
    "    )\n",
    "\n",
    "    model.summary(print_fn=logging.info)\n",
    "    return model\n",
    "\n",
    "def run_fn(fn_args: tfx.components.FnArgs):\n",
    "    \"\"\"\n",
    "    The callback function that will be called by the Trainer component\n",
    "    to train the model using the suplied arguments.\n",
    "\n",
    "    Args:\n",
    "        fn_args: A collection of name/value pairs representing the \n",
    "            arguments to train the model.\n",
    "    \"\"\"\n",
    "    tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)\n",
    "\n",
    "    train_dataset = _input_fn(\n",
    "        fn_args.train_files,\n",
    "        fn_args.data_accessor,\n",
    "        tf_transform_output,\n",
    "        batch_size=BATCH_SIZE,\n",
    "    )\n",
    "\n",
    "    eval_dataset = _input_fn(\n",
    "        fn_args.eval_files,\n",
    "        fn_args.data_accessor,\n",
    "        tf_transform_output,\n",
    "        batch_size=BATCH_SIZE,\n",
    "    )\n",
    "\n",
    "    model = _model()\n",
    "    model.fit(\n",
    "        train_dataset,\n",
    "        steps_per_epoch=fn_args.train_steps,\n",
    "        validation_data=eval_dataset,\n",
    "        validation_steps=fn_args.eval_steps,\n",
    "        epochs=EPOCHS\n",
    "    )\n",
    "\n",
    "    # We need to modify the default signature to include the transform layer in \n",
    "    # the computational graph.\n",
    "    signatures = {\n",
    "        \"serving_default\": _get_serve_tf_examples_fn(model, tf_transform_output),\n",
    "    }\n",
    "    model.save(fn_args.serving_model_dir, save_format=\"tf\", signatures=signatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "163eb2f4-ddb0-4763-a355-5ce59e7af405",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Generating ephemeral wheel package for '/home/jupyter/MLOps_Pipeline_project/trainer.py' (including modules: ['constants', 'trainer', 'transform']).\n",
      "INFO:absl:User module package has hash fingerprint version 00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '/tmp/tmpnsvwn5t7/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmphevotpor', '--dist-dir', '/tmp/tmp7e12j0dd']\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
      "  setuptools.SetuptoolsDeprecationWarning,\n",
      "INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'; target user module is 'trainer'.\n",
      "INFO:absl:Full user module path is 'trainer@/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'\n",
      "INFO:absl:Running driver for Trainer\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:44:16.607223  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:44:16.612802  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Running executor for Trainer\n",
      "INFO:absl:Train on the 'train' split when train_args.splits is not set.\n",
      "INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.\n",
      "INFO:absl:udf_utils.get_fn {'train_args': '{\\n  \"num_steps\": 100\\n}', 'eval_args': '{\\n  \"num_steps\": 5\\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'trainer@/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'} 'run_fn'\n",
      "INFO:absl:Installing '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp8gle6kl4', '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running bdist_wheel\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib\n",
      "copying constants.py -> build/lib\n",
      "copying trainer.py -> build/lib\n",
      "copying transform.py -> build/lib\n",
      "installing to /tmp/tmphevotpor\n",
      "running install\n",
      "running install_lib\n",
      "copying build/lib/constants.py -> /tmp/tmphevotpor\n",
      "copying build/lib/transform.py -> /tmp/tmphevotpor\n",
      "copying build/lib/trainer.py -> /tmp/tmphevotpor\n",
      "running install_egg_info\n",
      "running egg_info\n",
      "creating tfx_user_code_Trainer.egg-info\n",
      "writing tfx_user_code_Trainer.egg-info/PKG-INFO\n",
      "writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt\n",
      "writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt\n",
      "writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n",
      "reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n",
      "writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n",
      "Copying tfx_user_code_Trainer.egg-info to /tmp/tmphevotpor/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3.7.egg-info\n",
      "running install_scripts\n",
      "creating /tmp/tmphevotpor/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/WHEEL\n",
      "creating '/tmp/tmp7e12j0dd/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' and adding '/tmp/tmphevotpor' to it\n",
      "adding 'constants.py'\n",
      "adding 'trainer.py'\n",
      "adding 'transform.py'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/METADATA'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/WHEEL'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/top_level.txt'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/RECORD'\n",
      "removing /tmp/tmphevotpor\n",
      "Processing /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed '/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'.\n"
     ]
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     "text": [
      "Installing collected packages: tfx-user-code-Trainer\n",
      "Successfully installed tfx-user-code-Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04\n"
     ]
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     "text": [
      "INFO:absl:Training model.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n"
     ]
    },
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     "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"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
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     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
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      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
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      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
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      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Model: \"model\"\n",
      "INFO:absl:__________________________________________________________________________________________________\n",
      "INFO:absl: Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "INFO:absl:==================================================================================================\n",
      "INFO:absl: Age (InputLayer)               [(None, 1)]          0           []                               \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: EstimatedSalary (InputLayer)   [(None, 1)]          0           []                               \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: Gender (InputLayer)            [(None, 1)]          0           []                               \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: concatenate (Concatenate)      (None, 3)            0           ['Age[0][0]',                    \n",
      "INFO:absl:                                                                  'EstimatedSalary[0][0]',        \n",
      "INFO:absl:                                                                  'Gender[0][0]']                 \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: dense (Dense)                  (None, 8)            32          ['concatenate[0][0]']            \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: dense_1 (Dense)                (None, 8)            72          ['dense[0][0]']                  \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: dense_2 (Dense)                (None, 1)            9           ['dense_1[0][0]']                \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl:==================================================================================================\n",
      "INFO:absl:Total params: 113\n",
      "INFO:absl:Trainable params: 113\n",
      "INFO:absl:Non-trainable params: 0\n",
      "INFO:absl:__________________________________________________________________________________________________\n"
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     "text": [
      "Epoch 1/50\n",
      "100/100 [==============================] - 2s 3ms/step - loss: 799.0636 - binary_accuracy: 0.5200 - val_loss: 40.8924 - val_binary_accuracy: 0.3187\n",
      "Epoch 2/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 134.7023 - binary_accuracy: 0.5047 - val_loss: 19.3253 - val_binary_accuracy: 0.7188\n",
      "Epoch 3/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 90.3488 - binary_accuracy: 0.4894 - val_loss: 150.2175 - val_binary_accuracy: 0.7063\n",
      "Epoch 4/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 127.6688 - binary_accuracy: 0.4928 - val_loss: 52.0924 - val_binary_accuracy: 0.6938\n",
      "Epoch 5/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 89.0378 - binary_accuracy: 0.5159 - val_loss: 47.3812 - val_binary_accuracy: 0.6812\n",
      "Epoch 6/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 62.8836 - binary_accuracy: 0.5097 - val_loss: 93.6148 - val_binary_accuracy: 0.7000\n",
      "Epoch 7/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 75.3753 - binary_accuracy: 0.5156 - val_loss: 57.8620 - val_binary_accuracy: 0.2875\n",
      "Epoch 8/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 76.9506 - binary_accuracy: 0.4963 - val_loss: 135.8239 - val_binary_accuracy: 0.3125\n",
      "Epoch 9/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 74.0711 - binary_accuracy: 0.4978 - val_loss: 120.1648 - val_binary_accuracy: 0.3187\n",
      "Epoch 10/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 60.3178 - binary_accuracy: 0.5113 - val_loss: 6.4674 - val_binary_accuracy: 0.3187\n",
      "Epoch 11/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 45.7714 - binary_accuracy: 0.4925 - val_loss: 37.6554 - val_binary_accuracy: 0.7188\n",
      "Epoch 12/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 49.7717 - binary_accuracy: 0.5044 - val_loss: 4.8804 - val_binary_accuracy: 0.3063\n",
      "Epoch 13/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 57.0246 - binary_accuracy: 0.5156 - val_loss: 27.0369 - val_binary_accuracy: 0.2875\n",
      "Epoch 14/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 50.6055 - binary_accuracy: 0.5172 - val_loss: 113.5715 - val_binary_accuracy: 0.3187\n",
      "Epoch 15/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 44.1878 - binary_accuracy: 0.5231 - val_loss: 0.9861 - val_binary_accuracy: 0.7437\n",
      "Epoch 16/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 30.9257 - binary_accuracy: 0.5156 - val_loss: 62.1507 - val_binary_accuracy: 0.2750\n",
      "Epoch 17/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 65.9203 - binary_accuracy: 0.5138 - val_loss: 57.9260 - val_binary_accuracy: 0.3125\n",
      "Epoch 18/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 32.4403 - binary_accuracy: 0.5213 - val_loss: 11.4981 - val_binary_accuracy: 0.7188\n",
      "Epoch 19/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 41.7935 - binary_accuracy: 0.5278 - val_loss: 15.6725 - val_binary_accuracy: 0.6750\n",
      "Epoch 20/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 26.2753 - binary_accuracy: 0.5141 - val_loss: 23.9202 - val_binary_accuracy: 0.2750\n",
      "Epoch 21/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 18.3074 - binary_accuracy: 0.5103 - val_loss: 2.2504 - val_binary_accuracy: 0.6812\n",
      "Epoch 22/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 18.6211 - binary_accuracy: 0.5344 - val_loss: 2.7424 - val_binary_accuracy: 0.7063\n",
      "Epoch 23/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 23.0223 - binary_accuracy: 0.5247 - val_loss: 12.6611 - val_binary_accuracy: 0.7000\n",
      "Epoch 24/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 17.5501 - binary_accuracy: 0.5375 - val_loss: 20.5585 - val_binary_accuracy: 0.2875\n",
      "Epoch 25/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 20.0780 - binary_accuracy: 0.5050 - val_loss: 5.1374 - val_binary_accuracy: 0.3625\n",
      "Epoch 26/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 15.8843 - binary_accuracy: 0.5156 - val_loss: 7.2566 - val_binary_accuracy: 0.6750\n",
      "Epoch 27/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 19.0957 - binary_accuracy: 0.5206 - val_loss: 0.8709 - val_binary_accuracy: 0.8188\n",
      "Epoch 28/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 9.0975 - binary_accuracy: 0.5356 - val_loss: 9.0233 - val_binary_accuracy: 0.7000\n",
      "Epoch 29/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 12.3111 - binary_accuracy: 0.5300 - val_loss: 5.7276 - val_binary_accuracy: 0.3750\n",
      "Epoch 30/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 18.5947 - binary_accuracy: 0.5328 - val_loss: 17.7823 - val_binary_accuracy: 0.3187\n",
      "Epoch 31/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 12.4983 - binary_accuracy: 0.5241 - val_loss: 3.3420 - val_binary_accuracy: 0.3938\n",
      "Epoch 32/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 5.6189 - binary_accuracy: 0.5347 - val_loss: 1.6629 - val_binary_accuracy: 0.7000\n",
      "Epoch 33/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 6.5210 - binary_accuracy: 0.5384 - val_loss: 5.0515 - val_binary_accuracy: 0.3562\n",
      "Epoch 34/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 4.4936 - binary_accuracy: 0.5744 - val_loss: 1.2617 - val_binary_accuracy: 0.7188\n",
      "Epoch 35/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 1.7510 - binary_accuracy: 0.6459 - val_loss: 1.8966 - val_binary_accuracy: 0.7250\n",
      "Epoch 36/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 4.2860 - binary_accuracy: 0.5875 - val_loss: 5.2362 - val_binary_accuracy: 0.3625\n",
      "Epoch 37/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 2.8607 - binary_accuracy: 0.5838 - val_loss: 0.9897 - val_binary_accuracy: 0.6375\n",
      "Epoch 38/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 2.9171 - binary_accuracy: 0.5741 - val_loss: 2.7288 - val_binary_accuracy: 0.7063\n",
      "Epoch 39/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 1.8398 - binary_accuracy: 0.6366 - val_loss: 3.5211 - val_binary_accuracy: 0.3875\n",
      "Epoch 40/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 2.2204 - binary_accuracy: 0.5916 - val_loss: 1.3973 - val_binary_accuracy: 0.7000\n",
      "Epoch 41/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 1.9295 - binary_accuracy: 0.6191 - val_loss: 1.1416 - val_binary_accuracy: 0.7188\n",
      "Epoch 42/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.8934 - binary_accuracy: 0.7047 - val_loss: 0.4898 - val_binary_accuracy: 0.8750\n",
      "Epoch 43/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 3.9771 - binary_accuracy: 0.5566 - val_loss: 8.0240 - val_binary_accuracy: 0.3812\n",
      "Epoch 44/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 1.5864 - binary_accuracy: 0.6566 - val_loss: 4.7688 - val_binary_accuracy: 0.3938\n",
      "Epoch 45/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 1.1834 - binary_accuracy: 0.6831 - val_loss: 1.0114 - val_binary_accuracy: 0.5312\n",
      "Epoch 46/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 1.2027 - binary_accuracy: 0.6259 - val_loss: 0.7988 - val_binary_accuracy: 0.7188\n",
      "Epoch 47/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.9088 - binary_accuracy: 0.6712 - val_loss: 0.8404 - val_binary_accuracy: 0.7125\n",
      "Epoch 48/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 2.5357 - binary_accuracy: 0.6150 - val_loss: 0.7505 - val_binary_accuracy: 0.6812\n",
      "Epoch 49/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6852 - binary_accuracy: 0.7469 - val_loss: 0.4885 - val_binary_accuracy: 0.8375\n",
      "Epoch 50/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.7551 - binary_accuracy: 0.7150 - val_loss: 0.5414 - val_binary_accuracy: 0.7563\n",
      "INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7/Format-Serving/assets\n"
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      "INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7/Format-Serving/assets\n",
      "INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7/Format-Serving. ModelRun written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model_run/7\n",
      "INFO:absl:Running publisher for Trainer\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
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       "<script>\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7fa91fa805d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['transform_graph']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'TransformGraph'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae6450</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">TransformGraph</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
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       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'TransformGraph'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6)<span class=\"deemphasize\"> at 0x7fa9bca826d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.TransformGraph&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
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       ".tfx-object, .tfx-object * {\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Model'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91d5457d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Model</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object .class-name {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Model'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7)<span class=\"deemphasize\"> at 0x7fa9bcb0ded0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Model&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['model_run']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelRun'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91d545cd0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelRun</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
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       ".tfx-object, .tfx-object * {\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ModelRun'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model_run/7)<span class=\"deemphasize\"> at 0x7fa9bcb0d550</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ModelRun&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model_run/7</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.exec_properties</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['train_args']</td><td class = \"attrvalue\">{\n",
       "  &quot;num_steps&quot;: 100\n",
       "}</td></tr><tr><td class=\"attr-name\">['eval_args']</td><td class = \"attrvalue\">{\n",
       "  &quot;num_steps&quot;: 5\n",
       "}</td></tr><tr><td class=\"attr-name\">['module_file']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['run_fn']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['trainer_fn']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['custom_config']</td><td class = \"attrvalue\">null</td></tr><tr><td class=\"attr-name\">['module_path']</td><td class = \"attrvalue\">trainer@/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl</td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">.component.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['examples']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       "  background: white;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91f6b9a10</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Examples</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7fa91fa805d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['transform_graph']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'TransformGraph'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcae6450</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">TransformGraph</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'TransformGraph'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6)<span class=\"deemphasize\"> at 0x7fa9bca826d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.TransformGraph&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Transform/transform_graph/6</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Model'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91d5457d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Model</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Model'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7)<span class=\"deemphasize\"> at 0x7fa9bcb0ded0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Model&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['model_run']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelRun'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91d545cd0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelRun</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ModelRun'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model_run/7)<span class=\"deemphasize\"> at 0x7fa9bcb0d550</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ModelRun&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model_run/7</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: Trainer\n",
       "    execution_id: 7\n",
       "    outputs:\n",
       "        model: Channel(\n",
       "            type_name: Model\n",
       "            artifacts: [Artifact(artifact: id: 14\n",
       "        type_id: 26\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"model\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Trainer\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 26\n",
       "        name: \"Model\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        model_run: Channel(\n",
       "            type_name: ModelRun\n",
       "            artifacts: [Artifact(artifact: id: 15\n",
       "        type_id: 27\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model_run/7\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"model_run\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Trainer\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 27\n",
       "        name: \"ModelRun\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer = tfx.components.Trainer(\n",
    "    examples=example_gen.outputs[\"examples\"],\n",
    "    transform_graph=transform.outputs[\"transform_graph\"],\n",
    "    train_args=tfx.proto.TrainArgs(num_steps=100),\n",
    "    eval_args=tfx.proto.EvalArgs(num_steps=5),\n",
    "    module_file=os.path.abspath(TRAINER_MODULE_PATH),\n",
    ")\n",
    "context.run(trainer, enable_cache=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bea5695-d1e3-4f20-817e-21684ae3372d",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9b8fd50-9018-43b7-9563-7a9fa74fcdc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_config = tfma.EvalConfig(\n",
    "    model_specs=[\n",
    "                 tfma.ModelSpec(\n",
    "                     signature_name=\"serving_default\",\n",
    "                     preprocessing_function_names=['tft_layer'],\n",
    "                     label_key=\"Purchased\",\n",
    "                 )\n",
    "    ],\n",
    "    metrics_specs = [\n",
    "                     tfma.MetricsSpec(\n",
    "                         per_slice_thresholds={\n",
    "                             \"binary_accuracy\": tfma.PerSliceMetricThresholds(\n",
    "                                 thresholds=[\n",
    "                                             tfma.PerSliceMetricThreshold(\n",
    "                                                 slicing_specs=[tfma.SlicingSpec()],\n",
    "                                                 threshold=tfma.MetricThreshold(\n",
    "                                                     value_threshold=tfma.GenericValueThreshold(\n",
    "                                                         lower_bound={\"value\":0.7}\n",
    "                                                     ),\n",
    "                                                     change_threshold=tfma.GenericChangeThreshold(\n",
    "                                                         direction=tfma.MetricDirection.HIGHER_IS_BETTER,\n",
    "                                                         absolute={\"value\": -1e-10},\n",
    "                                                     ),\n",
    "                                                 ),\n",
    "                                             )\n",
    "                                 ]\n",
    "                             ),\n",
    "                         }\n",
    "                     )\n",
    "    ],\n",
    "    slicing_specs=[\n",
    "                   tfma.SlicingSpec(),\n",
    "                   tfma.SlicingSpec(feature_keys=[\"Gender\"])\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "82481559-cf08-4ca7-a0ad-66e71c446277",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Running driver for latest_blessed_model_resolver\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Running publisher for latest_blessed_model_resolver\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
     ]
    },
    {
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       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelBlessing'</span> (0 artifacts)<span class=\"deemphasize\"> at 0x7fa9bcb39f50</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelBlessing</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\">[]</td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: latest_blessed_model_resolver\n",
       "    execution_id: 8\n",
       "    outputs:\n",
       "        model: Channel(\n",
       "            type_name: Model\n",
       "            artifacts: []\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        model_blessings: Channel(\n",
       "            type_name: ModelBlessing\n",
       "            artifacts: []\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_resolver = tfx.dsl.Resolver(\n",
    "    strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,\n",
    "    model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),\n",
    "    model_blessings=tfx.dsl.Channel(type=tfx.types.standard_artifacts.ModelBlessing),\n",
    ").with_id(\"latest_blessed_model_resolver\")\n",
    "\n",
    "context.run(model_resolver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f7624c3-012c-40e2-8742-38fc55fa61c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Running driver for Evaluator\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:46:43.490140  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Running executor for Evaluator\n",
      "I0312 05:46:43.495919  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Nonempty beam arg extra_packages already includes dependency\n",
      "INFO:absl:udf_utils.get_fn {'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"per_slice_thresholds\": {\\n        \"binary_accuracy\": {\\n          \"thresholds\": [\\n            {\\n              \"slicing_specs\": [\\n                {}\\n              ],\\n              \"threshold\": {\\n                \"change_threshold\": {\\n                  \"absolute\": -1e-10,\\n                  \"direction\": \"HIGHER_IS_BETTER\"\\n                },\\n                \"value_threshold\": {\\n                  \"lower_bound\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"Purchased\",\\n      \"preprocessing_function_names\": [\\n        \"tft_layer\"\\n      ],\\n      \"signature_name\": \"serving_default\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"Gender\"\\n      ]\\n    }\\n  ]\\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} '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",
      "  signature_name: \"serving_default\"\n",
      "  label_key: \"Purchased\"\n",
      "  preprocessing_function_names: \"tft_layer\"\n",
      "}\n",
      "slicing_specs {\n",
      "}\n",
      "slicing_specs {\n",
      "  feature_keys: \"Gender\"\n",
      "}\n",
      "metrics_specs {\n",
      "  per_slice_thresholds {\n",
      "    key: \"binary_accuracy\"\n",
      "    value {\n",
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      "        threshold {\n",
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      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:Using /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7/Format-Serving as  model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bcbc5c50> and <keras.engine.input_layer.InputLayer object at 0x7fa91d8a2850>).\n"
     ]
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bcbc5c50> and <keras.engine.input_layer.InputLayer object at 0x7fa91d8a2850>).\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 {'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"per_slice_thresholds\": {\\n        \"binary_accuracy\": {\\n          \"thresholds\": [\\n            {\\n              \"slicing_specs\": [\\n                {}\\n              ],\\n              \"threshold\": {\\n                \"change_threshold\": {\\n                  \"absolute\": -1e-10,\\n                  \"direction\": \"HIGHER_IS_BETTER\"\\n                },\\n                \"value_threshold\": {\\n                  \"lower_bound\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"Purchased\",\\n      \"preprocessing_function_names\": [\\n        \"tft_layer\"\\n      ],\\n      \"signature_name\": \"serving_default\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"Gender\"\\n      ]\\n    }\\n  ]\\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} '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",
      "  signature_name: \"serving_default\"\n",
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      "\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",
      "  signature_name: \"serving_default\"\n",
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      "\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",
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      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bcf49250> and <keras.engine.input_layer.InputLayer object at 0x7fa91db31410>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bcf49250> and <keras.engine.input_layer.InputLayer object at 0x7fa91db31410>).\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bc8d2710> and <keras.engine.input_layer.InputLayer object at 0x7fa9bc967cd0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bc8d2710> and <keras.engine.input_layer.InputLayer object at 0x7fa9bc967cd0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa91d7e9a90> and <keras.engine.input_layer.InputLayer object at 0x7fa9510579d0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa91d7e9a90> and <keras.engine.input_layer.InputLayer object at 0x7fa9510579d0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bcbc5810> and <keras.engine.input_layer.InputLayer object at 0x7fa94fa25390>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bcbc5810> and <keras.engine.input_layer.InputLayer object at 0x7fa94fa25390>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa91da18a90> and <keras.engine.input_layer.InputLayer object at 0x7fa94fb8fcd0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa91da18a90> and <keras.engine.input_layer.InputLayer object at 0x7fa94fb8fcd0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8f8032750> and <keras.engine.input_layer.InputLayer object at 0x7fa8f808f350>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8f8032750> and <keras.engine.input_layer.InputLayer object at 0x7fa8f808f350>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8dddf4ad0> and <keras.engine.input_layer.InputLayer object at 0x7fa8ddde1990>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8dddf4ad0> and <keras.engine.input_layer.InputLayer object at 0x7fa8ddde1990>).\n",
      "INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/evaluation/9.\n",
      "INFO:absl:Checking validation results.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:114: 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 /opt/conda/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:114: 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 /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/blessing/9.\n",
      "INFO:absl:Running publisher for Evaluator\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
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    },
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91f6b9a10</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Examples</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object, .tfx-object * {\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7fa91fa805d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object, .tfx-object * {\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object .class-name {\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Model'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91d5457d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Model</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
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       ".tfx-object .deemphasize {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Model'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7)<span class=\"deemphasize\"> at 0x7fa9bcb0ded0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Model&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['baseline_model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       "  display: block;\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Model'</span> (0 artifacts)<span class=\"deemphasize\"> at 0x7fa94fbc6dd0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Model</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\">[]</td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['evaluation']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelEvaluation'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcbe39d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelEvaluation</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ModelEvaluation'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/evaluation/9)<span class=\"deemphasize\"> at 0x7fa91da380d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ModelEvaluation&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/evaluation/9</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['blessing']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelBlessing'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcbe3e90</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelBlessing</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
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       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: Evaluator\n",
       "    execution_id: 9\n",
       "    outputs:\n",
       "        evaluation: Channel(\n",
       "            type_name: ModelEvaluation\n",
       "            artifacts: [Artifact(artifact: id: 16\n",
       "        type_id: 30\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/evaluation/9\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"evaluation\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Evaluator\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 30\n",
       "        name: \"ModelEvaluation\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        blessing: Channel(\n",
       "            type_name: ModelBlessing\n",
       "            artifacts: [Artifact(artifact: id: 17\n",
       "        type_id: 31\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/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: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"current_model_id\"\n",
       "          value {\n",
       "            int_value: 14\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"blessing\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Evaluator\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 31\n",
       "        name: \"ModelBlessing\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = tfx.components.Evaluator(\n",
    "    examples=example_gen.outputs[\"examples\"],\n",
    "    model=trainer.outputs[\"model\"],\n",
    "    eval_config=eval_config,\n",
    "    baseline_model=model_resolver.outputs[\"model\"],\n",
    ")\n",
    "\n",
    "context.run(evaluator, enable_cache=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46ae8b6-3725-4a20-b4e9-81f08f1997a1",
   "metadata": {},
   "source": [
    "### Pushing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8daa78e5-b25e-4326-952c-440d81b97a07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Running driver for Pusher\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:47:20.783195  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Running executor for Pusher\n",
      "INFO:absl:Model version: 1647064040\n",
      "INFO:absl:Model written to serving path model/1647064040.\n",
      "INFO:absl:Model pushed to /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Pusher/pushed_model/10.\n",
      "INFO:absl:Running publisher for Pusher\n",
      "INFO:absl:MetadataStore with DB connection initialized\n"
     ]
    },
    {
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       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelBlessing'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcbe3e90</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelBlessing</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ModelBlessing'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/blessing/9)<span class=\"deemphasize\"> at 0x7fa91d509410</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ModelBlessing&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/blessing/9</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['pushed_model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'PushedModel'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa8dd5cb810</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">PushedModel</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'PushedModel'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Pusher/pushed_model/10)<span class=\"deemphasize\"> at 0x7fa94faed750</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.PushedModel&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Pusher/pushed_model/10</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.exec_properties</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['push_destination']</td><td class = \"attrvalue\">{\n",
       "  &quot;filesystem&quot;: {\n",
       "    &quot;base_directory&quot;: &quot;model&quot;\n",
       "  }\n",
       "}</td></tr><tr><td class=\"attr-name\">['custom_config']</td><td class = \"attrvalue\">null</td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">.component.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Model'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa91d5457d0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Model</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Model'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7)<span class=\"deemphasize\"> at 0x7fa9bcb0ded0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Model&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Trainer/model/7</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['model_blessing']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ModelBlessing'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa9bcbe3e90</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ModelBlessing</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ModelBlessing'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/blessing/9)<span class=\"deemphasize\"> at 0x7fa91d509410</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ModelBlessing&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Evaluator/blessing/9</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['pushed_model']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'PushedModel'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7fa8dd5cb810</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">PushedModel</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'PushedModel'</span> (uri: /tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Pusher/pushed_model/10)<span class=\"deemphasize\"> at 0x7fa94faed750</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.PushedModel&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Pusher/pushed_model/10</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: Pusher\n",
       "    execution_id: 10\n",
       "    outputs:\n",
       "        pushed_model: Channel(\n",
       "            type_name: PushedModel\n",
       "            artifacts: [Artifact(artifact: id: 18\n",
       "        type_id: 33\n",
       "        uri: \"/tmp/tfx-interactive-2022-03-12T05_26_49.911729-96yqlmm1/Pusher/pushed_model/10\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"pushed_model\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Pusher\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"pushed\"\n",
       "          value {\n",
       "            int_value: 1\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"pushed_destination\"\n",
       "          value {\n",
       "            string_value: \"model/1647064040\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"pushed_version\"\n",
       "          value {\n",
       "            string_value: \"1647064040\"\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: \"1.4.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 33\n",
       "        name: \"PushedModel\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pusher = tfx.components.Pusher(\n",
    "    model=trainer.outputs[\"model\"],\n",
    "    model_blessing=evaluator.outputs[\"blessing\"],\n",
    "    push_destination=tfx.proto.PushDestination(\n",
    "        filesystem=tfx.proto.PushDestination.Filesystem(\n",
    "            base_directory=str(MODEL_DIRECTORY)\n",
    "        )\n",
    "    ),\n",
    ")\n",
    "\n",
    "context.run(pusher)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494c0a47-c54b-4a9b-80af-ceb75131f47c",
   "metadata": {},
   "source": [
    "### Inferencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cfb1eb46-154f-4e10-99ae-e7ea8b18f5e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_inference_fn(model_directory):\n",
    "    model_directories = (d for d in os.scandir(model_directory) if d.is_dir())\n",
    "    model_path = max(model_directories, key=lambda i: int(i.name)).path\n",
    "\n",
    "    loaded_model = tf.keras.models.load_model(model_path)\n",
    "\n",
    "    return loaded_model.signatures[\"serving_default\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "969620ca-6726-4a66-afbb-030d27bbf108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8dd60f250> and <keras.engine.input_layer.InputLayer object at 0x7fa8dd618190>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8dd60f250> and <keras.engine.input_layer.InputLayer object at 0x7fa8dd618190>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.3264355 ]\n",
      " [0.97265285]\n",
      " [0.6270244 ]\n",
      " [0.44954094]\n",
      " [0.6055691 ]\n",
      " [0.43268377]\n",
      " [0.9931779 ]\n",
      " [0.9590658 ]\n",
      " [0.9381055 ]\n",
      " [0.16483685]\n",
      " [0.00963226]\n",
      " [0.17854801]\n",
      " [0.5440971 ]\n",
      " [0.12227309]\n",
      " [0.20295498]\n",
      " [0.99399805]\n",
      " [0.31204042]\n",
      " [0.779172  ]\n",
      " [0.4713493 ]\n",
      " [0.06559923]\n",
      " [0.99009776]\n",
      " [0.98698556]\n",
      " [0.06094879]\n",
      " [0.60351074]\n",
      " [0.07195705]\n",
      " [0.6666118 ]\n",
      " [0.92699564]\n",
      " [0.9809886 ]\n",
      " [0.25242084]\n",
      " [0.5700159 ]\n",
      " [0.28742206]\n",
      " [0.30117548]\n",
      " [0.11593023]\n",
      " [0.23361868]\n",
      " [0.44971702]\n",
      " [0.167728  ]\n",
      " [0.8054378 ]\n",
      " [0.18791357]\n",
      " [0.17147547]\n",
      " [0.11504939]\n",
      " [0.04695523]\n",
      " [0.99569154]\n",
      " [0.8914021 ]\n",
      " [0.99695516]\n",
      " [0.01810947]\n",
      " [0.9591758 ]\n",
      " [0.55371517]\n",
      " [0.06376794]\n",
      " [0.17280924]\n",
      " [0.05554444]\n",
      " [0.46687847]\n",
      " [0.0558475 ]\n",
      " [0.18377075]\n",
      " [0.24303532]\n",
      " [0.9293905 ]\n",
      " [0.19258127]\n",
      " [0.8423981 ]\n",
      " [0.0571115 ]\n",
      " [0.09974855]\n",
      " [0.00706607]\n",
      " [0.32770076]\n",
      " [0.13857907]\n",
      " [0.9964013 ]\n",
      " [0.33311385]\n",
      " [0.49687067]\n",
      " [0.7889198 ]\n",
      " [0.18616706]\n",
      " [0.93134916]\n",
      " [0.6897022 ]\n",
      " [0.23582542]\n",
      " [0.59192103]\n",
      " [0.89851034]\n",
      " [0.9869486 ]\n",
      " [0.51845723]\n",
      " [0.7017367 ]\n",
      " [0.9965995 ]\n",
      " [0.632553  ]\n",
      " [0.88693655]\n",
      " [0.40275878]\n",
      " [0.00451452]\n",
      " [0.9087225 ]\n",
      " [0.20342189]\n",
      " [0.9832604 ]\n",
      " [0.92737937]\n",
      " [0.2992049 ]\n",
      " [0.11593023]\n",
      " [0.30957186]\n",
      " [0.8826796 ]\n",
      " [0.02553624]\n",
      " [0.30573913]\n",
      " [0.16812724]\n",
      " [0.05259797]\n",
      " [0.5299444 ]\n",
      " [0.13358647]\n",
      " [0.14004865]\n",
      " [0.59849703]\n",
      " [0.7322469 ]\n",
      " [0.92511976]\n",
      " [0.22522795]\n",
      " [0.00535342]\n",
      " [0.4647228 ]\n",
      " [0.26694855]\n",
      " [0.23699781]\n",
      " [0.9962913 ]\n",
      " [0.82442254]\n",
      " [0.17280924]\n",
      " [0.02429476]\n",
      " [0.23751694]\n",
      " [0.99875784]\n",
      " [0.3963827 ]\n",
      " [0.34928963]\n",
      " [0.9926255 ]\n",
      " [0.55371517]\n",
      " [0.9904445 ]\n",
      " [0.99217534]\n",
      " [0.44954094]\n",
      " [0.1940291 ]\n",
      " [0.05142778]\n",
      " [0.21388224]\n",
      " [0.28742206]]\n"
     ]
    }
   ],
   "source": [
    "inference_fn = get_inference_fn(MODEL_DIRECTORY)\n",
    "result = inference_fn(examples=tf.constant(_examples(test_df)))\n",
    "print(result[\"output_0\"].numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c6bd04-84dc-41d6-9189-d14b2371d239",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4f58941-a378-49e2-8da8-35b2c164ce40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting pipelines/sample-pipeline/schema/schema.pbtxt\n"
     ]
    }
   ],
   "source": [
    "%%writefile {SCHEMA_FILE_NAME}\n",
    "\n",
    "feature {\n",
    "  name: \"Gender\"\n",
    "  type: BYTES\n",
    "  domain: \"Gender\"\n",
    "  presence {\n",
    "    min_fraction: 1.0\n",
    "    min_count: 1\n",
    "  }\n",
    "  shape {\n",
    "    dim {\n",
    "      size: 1\n",
    "    }\n",
    "  }\n",
    "}\n",
    "feature {\n",
    "  name: \"Age\"\n",
    "  type: INT\n",
    "  presence {\n",
    "    min_fraction: 1.0\n",
    "    min_count: 1\n",
    "  }\n",
    "  shape {\n",
    "    dim {\n",
    "      size: 1\n",
    "    }\n",
    "  }\n",
    "}\n",
    "feature {\n",
    "  name: \"EstimatedSalary\"\n",
    "  type: INT\n",
    "  presence {\n",
    "    min_fraction: 1.0\n",
    "    min_count: 1\n",
    "  }\n",
    "  shape {\n",
    "    dim {\n",
    "      size: 1\n",
    "    }\n",
    "  }\n",
    "}\n",
    "feature {\n",
    "  name: \"Purchased\"\n",
    "  type: INT\n",
    "  bool_domain {\n",
    "  }\n",
    "  presence {\n",
    "    min_fraction: 1.0\n",
    "    min_count: 1\n",
    "  }\n",
    "  not_in_environment: \"SERVING\"\n",
    "  shape {\n",
    "    dim {\n",
    "      size: 1\n",
    "    }\n",
    "  }\n",
    "}\n",
    "string_domain {\n",
    "  name: \"Gender\"\n",
    "  value: \"Female\"\n",
    "  value: \"Male\"\n",
    "}\n",
    "default_environment: \"TRAINING\"\n",
    "default_environment: \"SERVING\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec907a5-65d9-43b4-ade1-a272140c2f67",
   "metadata": {},
   "source": [
    "### Running Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "687e568a-829e-499d-b9c9-e13152f0792b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_model_analysis as tfma\n",
    "\n",
    "def create_pipeline(\n",
    "    pipeline_name: str,\n",
    "    pipeline_directory: str,\n",
    "    data_directory: str,\n",
    "    schema_path: str,\n",
    "    model_directory: str,\n",
    "    metadata_path: str,\n",
    "    transform_module_path: str,\n",
    "    trainer_module_path: str,\n",
    "    )-> tfx.dsl.Pipeline:\n",
    "\n",
    "    output = tfx.proto.Output(\n",
    "    split_config=example_gen_pb2.SplitConfig(splits=[\n",
    "                                                     tfx.proto.SplitConfig.Split(name=\"train\", hash_buckets=3),\n",
    "                                                     tfx.proto.SplitConfig.Split(name=\"eval\", hash_buckets=1)\n",
    "    ]))\n",
    "    example_gen = tfx.components.CsvExampleGen(input_base=SAVED_DATA, output_config=output)\n",
    "\n",
    "    statistics_gen = tfx.components.StatisticsGen(\n",
    "        examples=example_gen.outputs['examples']\n",
    "    )\n",
    "\n",
    "    schema_importer = tfx.dsl.Importer(\n",
    "        source_uri=str(SCHEMA_DIRECTORY),\n",
    "        artifact_type=tfx.types.standard_artifacts.Schema\n",
    "    ).with_id(\"schema_importer\")\n",
    "\n",
    "    example_validator = tfx.components.ExampleValidator(\n",
    "        statistics=statistics_gen.outputs['statistics'],\n",
    "        schema=schema_importer.outputs['result']\n",
    "    )\n",
    "\n",
    "    transform = tfx.components.Transform(\n",
    "        examples=example_gen.outputs['examples'],\n",
    "        schema=schema_importer.outputs['result'],\n",
    "        module_file=os.path.abspath(TRANSFORM_MODULE_PATH),\n",
    "    )\n",
    "\n",
    "    trainer = tfx.components.Trainer(\n",
    "        examples=example_gen.outputs[\"examples\"],\n",
    "        transform_graph=transform.outputs[\"transform_graph\"],\n",
    "        train_args=tfx.proto.TrainArgs(num_steps=100),\n",
    "        eval_args=tfx.proto.EvalArgs(num_steps=5),\n",
    "        module_file=os.path.abspath(TRAINER_MODULE_PATH),\n",
    "    )\n",
    "\n",
    "    eval_config = tfma.EvalConfig(\n",
    "        model_specs=[\n",
    "                    tfma.ModelSpec(\n",
    "                        signature_name=\"serving_default\",\n",
    "                        preprocessing_function_names=['tft_layer'],\n",
    "                        label_key=\"Purchased\",\n",
    "                    )\n",
    "        ],\n",
    "        metrics_specs = [\n",
    "                        tfma.MetricsSpec(\n",
    "                            per_slice_thresholds={\n",
    "                                \"binary_accuracy\": tfma.PerSliceMetricThresholds(\n",
    "                                    thresholds=[\n",
    "                                                tfma.PerSliceMetricThreshold(\n",
    "                                                    slicing_specs=[tfma.SlicingSpec()],\n",
    "                                                    threshold=tfma.MetricThreshold(\n",
    "                                                        value_threshold=tfma.GenericValueThreshold(\n",
    "                                                            lower_bound={\"value\":0.7}\n",
    "                                                        ),\n",
    "                                                        change_threshold=tfma.GenericChangeThreshold(\n",
    "                                                            direction=tfma.MetricDirection.HIGHER_IS_BETTER,\n",
    "                                                            absolute={\"value\": -1e-10},\n",
    "                                                        ),\n",
    "                                                    ),\n",
    "                                                )\n",
    "                                    ]\n",
    "                                ),\n",
    "                            }\n",
    "                        )\n",
    "        ],\n",
    "        slicing_specs=[\n",
    "                    tfma.SlicingSpec(),\n",
    "                    tfma.SlicingSpec(feature_keys=[\"Gender\"])\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    model_resolver = tfx.dsl.Resolver(\n",
    "        strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,\n",
    "        model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),\n",
    "        model_blessings=tfx.dsl.Channel(type=tfx.types.standard_artifacts.ModelBlessing),\n",
    "    ).with_id(\"latest_blessed_model_resolver\")\n",
    "\n",
    "    evaluator = tfx.components.Evaluator(\n",
    "        examples=example_gen.outputs[\"examples\"],\n",
    "        model=trainer.outputs[\"model\"],\n",
    "        eval_config=eval_config,\n",
    "        baseline_model=model_resolver.outputs[\"model\"],\n",
    "    )\n",
    "\n",
    "    pusher = tfx.components.Pusher(\n",
    "        model=trainer.outputs[\"model\"],\n",
    "        model_blessing=evaluator.outputs[\"blessing\"],\n",
    "        push_destination=tfx.proto.PushDestination(\n",
    "            filesystem=tfx.proto.PushDestination.Filesystem(\n",
    "                base_directory=str(MODEL_DIRECTORY)\n",
    "            )\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    components = [\n",
    "                  example_gen,\n",
    "                  statistics_gen,\n",
    "                  schema_importer,\n",
    "                  example_validator,\n",
    "                  transform,\n",
    "                  trainer,\n",
    "                  model_resolver,\n",
    "                  evaluator,\n",
    "                  pusher\n",
    "    ]\n",
    "\n",
    "    return tfx.dsl.Pipeline(\n",
    "        pipeline_name=pipeline_name,\n",
    "        pipeline_root=pipeline_directory,\n",
    "        metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(\n",
    "            metadata_path\n",
    "            ),\n",
    "            components=components,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8258b866-8d55-4413-ba38-cf8ec9a5fd83",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Excluding no splits because exclude_splits is not set.\n",
      "INFO:absl:Excluding no splits because exclude_splits is not set.\n",
      "INFO:absl:Generating ephemeral wheel package for '/home/jupyter/MLOps_Pipeline_project/transform.py' (including modules: ['constants', 'trainer', 'transform']).\n",
      "INFO:absl:User module package has hash fingerprint version 00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '/tmp/tmpu1g340lb/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp_4nliwb_', '--dist-dir', '/tmp/tmptb0pf_3z']\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
      "  setuptools.SetuptoolsDeprecationWarning,\n",
      "INFO:absl:Successfully built user code wheel distribution at 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'; target user module is 'transform'.\n",
      "INFO:absl:Full user module path is 'transform@pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'\n",
      "INFO:absl:Generating ephemeral wheel package for '/home/jupyter/MLOps_Pipeline_project/trainer.py' (including modules: ['constants', 'trainer', 'transform']).\n",
      "INFO:absl:User module package has hash fingerprint version 00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '/tmp/tmpccj0w4xg/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmptbrce0mv', '--dist-dir', '/tmp/tmpobc4900_']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running bdist_wheel\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib\n",
      "copying constants.py -> build/lib\n",
      "copying trainer.py -> build/lib\n",
      "copying transform.py -> build/lib\n",
      "installing to /tmp/tmp_4nliwb_\n",
      "running install\n",
      "running install_lib\n",
      "copying build/lib/constants.py -> /tmp/tmp_4nliwb_\n",
      "copying build/lib/transform.py -> /tmp/tmp_4nliwb_\n",
      "copying build/lib/trainer.py -> /tmp/tmp_4nliwb_\n",
      "running install_egg_info\n",
      "running egg_info\n",
      "creating tfx_user_code_Transform.egg-info\n",
      "writing tfx_user_code_Transform.egg-info/PKG-INFO\n",
      "writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt\n",
      "writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt\n",
      "writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n",
      "reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n",
      "writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n",
      "Copying tfx_user_code_Transform.egg-info to /tmp/tmp_4nliwb_/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3.7.egg-info\n",
      "running install_scripts\n",
      "creating /tmp/tmp_4nliwb_/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/WHEEL\n",
      "creating '/tmp/tmptb0pf_3z/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' and adding '/tmp/tmp_4nliwb_' to it\n",
      "adding 'constants.py'\n",
      "adding 'trainer.py'\n",
      "adding 'transform.py'\n",
      "adding 'tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/METADATA'\n",
      "adding 'tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/WHEEL'\n",
      "adding 'tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/top_level.txt'\n",
      "adding 'tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/RECORD'\n",
      "removing /tmp/tmp_4nliwb_\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
      "  setuptools.SetuptoolsDeprecationWarning,\n",
      "INFO:absl:Successfully built user code wheel distribution at 'pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'; target user module is 'trainer'.\n",
      "INFO:absl:Full user module path is 'trainer@pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'\n",
      "INFO:absl:Using deployment config:\n",
      " executor_specs {\n",
      "  key: \"CsvExampleGen\"\n",
      "  value {\n",
      "    beam_executable_spec {\n",
      "      python_executor_spec {\n",
      "        class_path: \"tfx.components.example_gen.csv_example_gen.executor.Executor\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "executor_specs {\n",
      "  key: \"Evaluator\"\n",
      "  value {\n",
      "    beam_executable_spec {\n",
      "      python_executor_spec {\n",
      "        class_path: \"tfx.components.evaluator.executor.Executor\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "executor_specs {\n",
      "  key: \"ExampleValidator\"\n",
      "  value {\n",
      "    python_class_executable_spec {\n",
      "      class_path: \"tfx.components.example_validator.executor.Executor\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "executor_specs {\n",
      "  key: \"Pusher\"\n",
      "  value {\n",
      "    python_class_executable_spec {\n",
      "      class_path: \"tfx.components.pusher.executor.Executor\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "executor_specs {\n",
      "  key: \"StatisticsGen\"\n",
      "  value {\n",
      "    beam_executable_spec {\n",
      "      python_executor_spec {\n",
      "        class_path: \"tfx.components.statistics_gen.executor.Executor\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "executor_specs {\n",
      "  key: \"Trainer\"\n",
      "  value {\n",
      "    python_class_executable_spec {\n",
      "      class_path: \"tfx.components.trainer.executor.GenericExecutor\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "executor_specs {\n",
      "  key: \"Transform\"\n",
      "  value {\n",
      "    beam_executable_spec {\n",
      "      python_executor_spec {\n",
      "        class_path: \"tfx.components.transform.executor.Executor\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "custom_driver_specs {\n",
      "  key: \"CsvExampleGen\"\n",
      "  value {\n",
      "    python_class_executable_spec {\n",
      "      class_path: \"tfx.components.example_gen.driver.FileBasedDriver\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "metadata_connection_config {\n",
      "  sqlite {\n",
      "    filename_uri: \"metadata/sample-pipeline/metadata.db\"\n",
      "    connection_mode: READWRITE_OPENCREATE\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:Using connection config:\n",
      " sqlite {\n",
      "  filename_uri: \"metadata/sample-pipeline/metadata.db\"\n",
      "  connection_mode: READWRITE_OPENCREATE\n",
      "}\n",
      "\n",
      "INFO:absl:Component CsvExampleGen is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.example_gen.csv_example_gen.component.CsvExampleGen\"\n",
      "  }\n",
      "  id: \"CsvExampleGen\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"examples\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\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",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"input_base\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"saved_data\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"input_config\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"{\\n  \\\"splits\\\": [\\n    {\\n      \\\"name\\\": \\\"single_split\\\",\\n      \\\"pattern\\\": \\\"*\\\"\\n    }\\n  ]\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"output_config\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"{\\n  \\\"split_config\\\": {\\n    \\\"splits\\\": [\\n      {\\n        \\\"hash_buckets\\\": 3,\\n        \\\"name\\\": \\\"train\\\"\\n      },\\n      {\\n        \\\"hash_buckets\\\": 1,\\n        \\\"name\\\": \\\"eval\\\"\\n      }\\n    ]\\n  }\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"output_data_format\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 6\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"output_file_format\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 5\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "downstream_nodes: \"Evaluator\"\n",
      "downstream_nodes: \"StatisticsGen\"\n",
      "downstream_nodes: \"Trainer\"\n",
      "downstream_nodes: \"Transform\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:34.565832  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:50:34.577617  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:50:34.587663  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:50:34.598805  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:select span and version = (0, None)\n",
      "INFO:absl:latest span and version = (0, None)\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:34.618485  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Going to run a new execution 1\n",
      "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: \"pipelines/sample-pipeline/CsvExampleGen/examples/1\"\n",
      "custom_properties {\n",
      "  key: \"input_fingerprint\"\n",
      "  value {\n",
      "    string_value: \"split:single_split,num_files:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:CsvExampleGen:examples:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"span\"\n",
      "  value {\n",
      "    int_value: 0\n",
      "  }\n",
      "}\n",
      ", artifact_type: 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",
      ")]}), exec_properties={'input_config': '{\\n  \"splits\": [\\n    {\\n      \"name\": \"single_split\",\\n      \"pattern\": \"*\"\\n    }\\n  ]\\n}', 'output_data_format': 6, 'output_file_format': 5, 'input_base': 'saved_data', 'output_config': '{\\n  \"split_config\": {\\n    \"splits\": [\\n      {\\n        \"hash_buckets\": 3,\\n        \"name\": \"train\"\\n      },\\n      {\\n        \"hash_buckets\": 1,\\n        \"name\": \"eval\"\\n      }\\n    ]\\n  }\\n}', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042'}, execution_output_uri='pipelines/sample-pipeline/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/CsvExampleGen/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.example_gen.csv_example_gen.component.CsvExampleGen\"\n",
      "  }\n",
      "  id: \"CsvExampleGen\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"examples\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\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",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"input_base\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"saved_data\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"input_config\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"{\\n  \\\"splits\\\": [\\n    {\\n      \\\"name\\\": \\\"single_split\\\",\\n      \\\"pattern\\\": \\\"*\\\"\\n    }\\n  ]\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"output_config\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"{\\n  \\\"split_config\\\": {\\n    \\\"splits\\\": [\\n      {\\n        \\\"hash_buckets\\\": 3,\\n        \\\"name\\\": \\\"train\\\"\\n      },\\n      {\\n        \\\"hash_buckets\\\": 1,\\n        \\\"name\\\": \\\"eval\\\"\\n      }\\n    ]\\n  }\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"output_data_format\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 6\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"output_file_format\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 5\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "downstream_nodes: \"Evaluator\"\n",
      "downstream_nodes: \"StatisticsGen\"\n",
      "downstream_nodes: \"Trainer\"\n",
      "downstream_nodes: \"Transform\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:Generating examples.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running bdist_wheel\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib\n",
      "copying constants.py -> build/lib\n",
      "copying trainer.py -> build/lib\n",
      "copying transform.py -> build/lib\n",
      "installing to /tmp/tmptbrce0mv\n",
      "running install\n",
      "running install_lib\n",
      "copying build/lib/constants.py -> /tmp/tmptbrce0mv\n",
      "copying build/lib/transform.py -> /tmp/tmptbrce0mv\n",
      "copying build/lib/trainer.py -> /tmp/tmptbrce0mv\n",
      "running install_egg_info\n",
      "running egg_info\n",
      "creating tfx_user_code_Trainer.egg-info\n",
      "writing tfx_user_code_Trainer.egg-info/PKG-INFO\n",
      "writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt\n",
      "writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt\n",
      "writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n",
      "reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n",
      "writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n",
      "Copying tfx_user_code_Trainer.egg-info to /tmp/tmptbrce0mv/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3.7.egg-info\n",
      "running install_scripts\n",
      "creating /tmp/tmptbrce0mv/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/WHEEL\n",
      "creating '/tmp/tmpobc4900_/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' and adding '/tmp/tmptbrce0mv' to it\n",
      "adding 'constants.py'\n",
      "adding 'trainer.py'\n",
      "adding 'transform.py'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/METADATA'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/WHEEL'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/top_level.txt'\n",
      "adding 'tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04.dist-info/RECORD'\n",
      "removing /tmp/tmptbrce0mv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Processing input csv data saved_data/* to TFExample.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n",
      "INFO:absl:Examples generated.\n",
      "INFO:absl:Cleaning up stateless execution info.\n",
      "INFO:absl:Execution 1 succeeded.\n",
      "INFO:absl:Cleaning up stateful execution info.\n",
      "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: \"pipelines/sample-pipeline/CsvExampleGen/examples/1\"\n",
      "custom_properties {\n",
      "  key: \"input_fingerprint\"\n",
      "  value {\n",
      "    string_value: \"split:single_split,num_files:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:CsvExampleGen:examples:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"span\"\n",
      "  value {\n",
      "    int_value: 0\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: 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",
      ")]}) for execution 1\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Component CsvExampleGen is finished.\n",
      "INFO:absl:Component latest_blessed_model_resolver is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.dsl.components.common.resolver.Resolver\"\n",
      "  }\n",
      "  id: \"latest_blessed_model_resolver\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.latest_blessed_model_resolver\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\n",
      "  inputs {\n",
      "    key: \"model\"\n",
      "    value {\n",
      "      channels {\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Model\"\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  inputs {\n",
      "    key: \"model_blessings\"\n",
      "    value {\n",
      "      channels {\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"ModelBlessing\"\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  resolver_config {\n",
      "    resolver_steps {\n",
      "      class_path: \"tfx.dsl.input_resolution.strategies.latest_blessed_model_strategy.LatestBlessedModelStrategy\"\n",
      "      config_json: \"{}\"\n",
      "      input_keys: \"model\"\n",
      "      input_keys: \"model_blessings\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "downstream_nodes: \"Evaluator\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:Running as an resolver node.\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "WARNING:absl:Artifact type ModelBlessing is not found in MLMD.\n",
      "WARNING:absl:Artifact type Model is not found in MLMD.\n",
      "I0312 05:50:35.887825  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component latest_blessed_model_resolver is finished.\n",
      "INFO:absl:Component schema_importer is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.dsl.components.common.importer.Importer\"\n",
      "  }\n",
      "  id: \"schema_importer\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.schema_importer\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"result\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"Schema\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"artifact_uri\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"pipelines/sample-pipeline/schema\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"reimport\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "downstream_nodes: \"ExampleValidator\"\n",
      "downstream_nodes: \"Transform\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:Running as an importer node.\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:35.954255  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Processing source uri: pipelines/sample-pipeline/schema, properties: {}, custom_properties: {}\n",
      "I0312 05:50:35.968235  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component schema_importer is finished.\n",
      "INFO:absl:Component StatisticsGen is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.statistics_gen.component.StatisticsGen\"\n",
      "  }\n",
      "  id: \"StatisticsGen\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.StatisticsGen\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"examples\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"statistics\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleStatistics\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"exclude_splits\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"[]\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"CsvExampleGen\"\n",
      "downstream_nodes: \"ExampleValidator\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:35.995680  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Going to run a new execution 4\n",
      "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={'examples': [Artifact(artifact: id: 1\n",
      "type_id: 15\n",
      "uri: \"pipelines/sample-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:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:CsvExampleGen:examples: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: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064235861\n",
      "last_update_time_since_epoch: 1647064235861\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",
      ")]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: \"pipelines/sample-pipeline/StatisticsGen/statistics/4\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:StatisticsGen:statistics:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/sample-pipeline/StatisticsGen/.system/executor_execution/4/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/StatisticsGen/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/StatisticsGen/.system/executor_execution/4/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.statistics_gen.component.StatisticsGen\"\n",
      "  }\n",
      "  id: \"StatisticsGen\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.StatisticsGen\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"examples\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"statistics\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleStatistics\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"exclude_splits\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"[]\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"CsvExampleGen\"\n",
      "downstream_nodes: \"ExampleValidator\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:Generating statistics for split train.\n",
      "INFO:absl:Statistics for split train written to pipelines/sample-pipeline/StatisticsGen/statistics/4/Split-train.\n",
      "INFO:absl:Generating statistics for split eval.\n",
      "INFO:absl:Statistics for split eval written to pipelines/sample-pipeline/StatisticsGen/statistics/4/Split-eval.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n",
      "INFO:absl:Cleaning up stateless execution info.\n",
      "INFO:absl:Execution 4 succeeded.\n",
      "INFO:absl:Cleaning up stateful execution info.\n",
      "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: \"pipelines/sample-pipeline/StatisticsGen/statistics/4\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:StatisticsGen:statistics:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}) for execution 4\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Component StatisticsGen is finished.\n",
      "INFO:absl:Component Transform is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.transform.component.Transform\"\n",
      "  }\n",
      "  id: \"Transform\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.Transform\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"examples\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "  inputs {\n",
      "    key: \"schema\"\n",
      "    value {\n",
      "      channels {\n",
      "        producer_node_query {\n",
      "          id: \"schema_importer\"\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.schema_importer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Schema\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"result\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"post_transform_anomalies\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleAnomalies\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"post_transform_schema\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"Schema\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"post_transform_stats\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleStatistics\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"pre_transform_schema\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"Schema\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"pre_transform_stats\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleStatistics\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"transform_graph\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"TransformGraph\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"transformed_examples\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\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",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"updated_analyzer_cache\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"TransformCache\"\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: \"disable_statistics\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"force_tf_compat_v1\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"module_path\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"transform@pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"CsvExampleGen\"\n",
      "upstream_nodes: \"schema_importer\"\n",
      "downstream_nodes: \"Trainer\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:38.936178  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Going to run a new execution 5\n",
      "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'schema': [Artifact(artifact: id: 2\n",
      "type_id: 18\n",
      "uri: \"pipelines/sample-pipeline/schema\"\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064235974\n",
      "last_update_time_since_epoch: 1647064235974\n",
      ", artifact_type: id: 18\n",
      "name: \"Schema\"\n",
      ")], 'examples': [Artifact(artifact: id: 1\n",
      "type_id: 15\n",
      "uri: \"pipelines/sample-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:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:CsvExampleGen:examples: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: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064235861\n",
      "last_update_time_since_epoch: 1647064235861\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",
      ")]}, output_dict=defaultdict(<class 'list'>, {'transform_graph': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/transform_graph/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:transform_graph:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"TransformGraph\"\n",
      ")], 'post_transform_anomalies': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/post_transform_anomalies/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:post_transform_anomalies:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleAnomalies\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")], 'post_transform_schema': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/post_transform_schema/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:post_transform_schema:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"Schema\"\n",
      ")], 'transformed_examples': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/transformed_examples/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:transformed_examples:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: 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",
      ")], 'post_transform_stats': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/post_transform_stats/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:post_transform_stats:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")], 'updated_analyzer_cache': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/updated_analyzer_cache/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:updated_analyzer_cache:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"TransformCache\"\n",
      ")], 'pre_transform_schema': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/pre_transform_schema/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:pre_transform_schema:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"Schema\"\n",
      ")], 'pre_transform_stats': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/pre_transform_stats/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:pre_transform_stats:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}), exec_properties={'disable_statistics': 0, 'force_tf_compat_v1': 0, 'custom_config': 'null', 'module_path': 'transform@pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'}, execution_output_uri='pipelines/sample-pipeline/Transform/.system/executor_execution/5/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/Transform/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/Transform/.system/executor_execution/5/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.transform.component.Transform\"\n",
      "  }\n",
      "  id: \"Transform\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.Transform\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"examples\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "  inputs {\n",
      "    key: \"schema\"\n",
      "    value {\n",
      "      channels {\n",
      "        producer_node_query {\n",
      "          id: \"schema_importer\"\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.schema_importer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Schema\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"result\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"post_transform_anomalies\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleAnomalies\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"post_transform_schema\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"Schema\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"post_transform_stats\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleStatistics\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"pre_transform_schema\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"Schema\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"pre_transform_stats\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleStatistics\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"transform_graph\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"TransformGraph\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"transformed_examples\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\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",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  outputs {\n",
      "    key: \"updated_analyzer_cache\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"TransformCache\"\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: \"disable_statistics\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"force_tf_compat_v1\"\n",
      "    value {\n",
      "      field_value {\n",
      "        int_value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"module_path\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"transform@pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"CsvExampleGen\"\n",
      "upstream_nodes: \"schema_importer\"\n",
      "downstream_nodes: \"Trainer\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.\n",
      "INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'transform@pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'\n",
      "INFO:absl:Installing 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmphaoqvrx8', 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl']\n",
      "E0312 05:50:38.978136350    3384 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'.\n",
      "INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'transform@pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'\n",
      "INFO:absl:Installing 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp76u0ic63', 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Transform\n",
      "Successfully installed tfx-user-code-Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E0312 05:50:42.073973361    3384 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'.\n",
      "INFO:absl:Installing 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpmacx3pui', 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Transform\n",
      "Successfully installed tfx-user-code-Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E0312 05:50:45.171892226    3384 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed 'pipelines/sample-pipeline/_wheels/tfx_user_code_Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Transform\n",
      "Successfully installed tfx-user-code-Transform-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: pipelines/sample-pipeline/Transform/transform_graph/5/.temp_path/tftransform_tmp/324fc489d2554e6fae63fea505a38fc8/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: pipelines/sample-pipeline/Transform/transform_graph/5/.temp_path/tftransform_tmp/324fc489d2554e6fae63fea505a38fc8/assets\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: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_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:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n",
      "INFO:absl:Cleaning up stateless execution info.\n",
      "INFO:absl:Execution 5 succeeded.\n",
      "INFO:absl:Cleaning up stateful execution info.\n",
      "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'transform_graph': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/transform_graph/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:transform_graph:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"TransformGraph\"\n",
      ")], 'post_transform_anomalies': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/post_transform_anomalies/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:post_transform_anomalies:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleAnomalies\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")], 'post_transform_schema': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/post_transform_schema/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:post_transform_schema:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"Schema\"\n",
      ")], 'transformed_examples': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/transformed_examples/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:transformed_examples:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: 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",
      ")], 'post_transform_stats': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/post_transform_stats/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:post_transform_stats:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")], 'updated_analyzer_cache': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/updated_analyzer_cache/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:updated_analyzer_cache:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"TransformCache\"\n",
      ")], 'pre_transform_schema': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/pre_transform_schema/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:pre_transform_schema:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"Schema\"\n",
      ")], 'pre_transform_stats': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Transform/pre_transform_stats/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:pre_transform_stats:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}) for execution 5\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:54.979737  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:50:55.010023  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component Transform is finished.\n",
      "INFO:absl:Component ExampleValidator is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.example_validator.component.ExampleValidator\"\n",
      "  }\n",
      "  id: \"ExampleValidator\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.ExampleValidator\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\n",
      "  inputs {\n",
      "    key: \"schema\"\n",
      "    value {\n",
      "      channels {\n",
      "        producer_node_query {\n",
      "          id: \"schema_importer\"\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.schema_importer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Schema\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"result\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "  inputs {\n",
      "    key: \"statistics\"\n",
      "    value {\n",
      "      channels {\n",
      "        producer_node_query {\n",
      "          id: \"StatisticsGen\"\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.StatisticsGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"ExampleStatistics\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"statistics\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"anomalies\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleAnomalies\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"exclude_splits\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"[]\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"StatisticsGen\"\n",
      "upstream_nodes: \"schema_importer\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:55.049024  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Going to run a new execution 6\n",
      "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'schema': [Artifact(artifact: id: 2\n",
      "type_id: 18\n",
      "uri: \"pipelines/sample-pipeline/schema\"\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064235974\n",
      "last_update_time_since_epoch: 1647064235974\n",
      ", artifact_type: id: 18\n",
      "name: \"Schema\"\n",
      ")], 'statistics': [Artifact(artifact: id: 3\n",
      "type_id: 20\n",
      "uri: \"pipelines/sample-pipeline/StatisticsGen/statistics/4\"\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value {\n",
      "    string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:StatisticsGen:statistics:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064238909\n",
      "last_update_time_since_epoch: 1647064238909\n",
      ", artifact_type: id: 20\n",
      "name: \"ExampleStatistics\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}, output_dict=defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: \"pipelines/sample-pipeline/ExampleValidator/anomalies/6\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:ExampleValidator:anomalies:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleAnomalies\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/sample-pipeline/ExampleValidator/.system/executor_execution/6/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/ExampleValidator/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/ExampleValidator/.system/executor_execution/6/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.example_validator.component.ExampleValidator\"\n",
      "  }\n",
      "  id: \"ExampleValidator\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.ExampleValidator\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\n",
      "  inputs {\n",
      "    key: \"schema\"\n",
      "    value {\n",
      "      channels {\n",
      "        producer_node_query {\n",
      "          id: \"schema_importer\"\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.schema_importer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Schema\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"result\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "  inputs {\n",
      "    key: \"statistics\"\n",
      "    value {\n",
      "      channels {\n",
      "        producer_node_query {\n",
      "          id: \"StatisticsGen\"\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.StatisticsGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"ExampleStatistics\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"statistics\"\n",
      "      }\n",
      "      min_count: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "outputs {\n",
      "  outputs {\n",
      "    key: \"anomalies\"\n",
      "    value {\n",
      "      artifact_spec {\n",
      "        type {\n",
      "          name: \"ExampleAnomalies\"\n",
      "          properties {\n",
      "            key: \"span\"\n",
      "            value: INT\n",
      "          }\n",
      "          properties {\n",
      "            key: \"split_names\"\n",
      "            value: STRING\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  parameters {\n",
      "    key: \"exclude_splits\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"[]\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"StatisticsGen\"\n",
      "upstream_nodes: \"schema_importer\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:Validating schema against the computed statistics for split train.\n",
      "INFO:absl:Validation complete for split train. Anomalies written to pipelines/sample-pipeline/ExampleValidator/anomalies/6/Split-train.\n",
      "INFO:absl:Validating schema against the computed statistics for split eval.\n",
      "INFO:absl:Validation complete for split eval. Anomalies written to pipelines/sample-pipeline/ExampleValidator/anomalies/6/Split-eval.\n",
      "INFO:absl:Cleaning up stateless execution info.\n",
      "INFO:absl:Execution 6 succeeded.\n",
      "INFO:absl:Cleaning up stateful execution info.\n",
      "INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: \"pipelines/sample-pipeline/ExampleValidator/anomalies/6\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:ExampleValidator:anomalies:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ExampleAnomalies\"\n",
      "properties {\n",
      "  key: \"span\"\n",
      "  value: INT\n",
      "}\n",
      "properties {\n",
      "  key: \"split_names\"\n",
      "  value: STRING\n",
      "}\n",
      ")]}) for execution 6\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Component ExampleValidator is finished.\n",
      "INFO:absl:Component Trainer is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.trainer.component.Trainer\"\n",
      "  }\n",
      "  id: \"Trainer\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.Trainer\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"examples\"\n",
      "      }\n",
      "      min_count: 1\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-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",
      "        }\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\\\": 5\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"module_path\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"trainer@pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"train_args\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"{\\n  \\\"num_steps\\\": 100\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"CsvExampleGen\"\n",
      "upstream_nodes: \"Transform\"\n",
      "downstream_nodes: \"Evaluator\"\n",
      "downstream_nodes: \"Pusher\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:50:55.127796  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "INFO:absl:Going to run a new execution 7\n",
      "INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=7, input_dict={'transform_graph': [Artifact(artifact: id: 4\n",
      "type_id: 22\n",
      "uri: \"pipelines/sample-pipeline/Transform/transform_graph/5\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Transform:transform_graph:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064255017\n",
      "last_update_time_since_epoch: 1647064255017\n",
      ", artifact_type: id: 22\n",
      "name: \"TransformGraph\"\n",
      ")], 'examples': [Artifact(artifact: id: 1\n",
      "type_id: 15\n",
      "uri: \"pipelines/sample-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:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:CsvExampleGen:examples: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: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064235861\n",
      "last_update_time_since_epoch: 1647064235861\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",
      ")]}, output_dict=defaultdict(<class 'list'>, {'model_run': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Trainer/model_run/7\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Trainer:model_run:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ModelRun\"\n",
      ")], 'model': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Trainer/model/7\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Trainer:model:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"Model\"\n",
      ")]}), exec_properties={'custom_config': 'null', 'module_path': 'trainer@pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl', 'eval_args': '{\\n  \"num_steps\": 5\\n}', 'train_args': '{\\n  \"num_steps\": 100\\n}'}, execution_output_uri='pipelines/sample-pipeline/Trainer/.system/executor_execution/7/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/Trainer/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/Trainer/.system/executor_execution/7/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.trainer.component.Trainer\"\n",
      "  }\n",
      "  id: \"Trainer\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline.Trainer\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "inputs {\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\n",
      "          }\n",
      "        }\n",
      "        output_key: \"examples\"\n",
      "      }\n",
      "      min_count: 1\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-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",
      "        }\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\\\": 5\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"module_path\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"trainer@pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  parameters {\n",
      "    key: \"train_args\"\n",
      "    value {\n",
      "      field_value {\n",
      "        string_value: \"{\\n  \\\"num_steps\\\": 100\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"CsvExampleGen\"\n",
      "upstream_nodes: \"Transform\"\n",
      "downstream_nodes: \"Evaluator\"\n",
      "downstream_nodes: \"Pusher\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:Train on the 'train' split when train_args.splits is not set.\n",
      "INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.\n",
      "INFO:absl:udf_utils.get_fn {'custom_config': 'null', 'module_path': 'trainer@pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl', 'eval_args': '{\\n  \"num_steps\": 5\\n}', 'train_args': '{\\n  \"num_steps\": 100\\n}'} 'run_fn'\n",
      "INFO:absl:Installing 'pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl' to a temporary directory.\n",
      "INFO:absl:Executing: ['/opt/conda/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp_vv0f9xq', 'pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl']\n",
      "E0312 05:50:55.165212687    3384 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Successfully installed 'pipelines/sample-pipeline/_wheels/tfx_user_code_Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04-py3-none-any.whl'.\n",
      "INFO:absl:Training model.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tfx-user-code-Trainer\n",
      "Successfully installed tfx-user-code-Trainer-0.0+00ab6b6a4a2cdb18b4cab7250e7889247768192a89405b0f424a325945eb3a04\n",
      "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:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n",
      "INFO:absl:Feature Gender has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Age has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature EstimatedSalary has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Feature Purchased has a shape dim {\n",
      "  size: 1\n",
      "}\n",
      ". Setting to DenseTensor.\n",
      "INFO:absl:Model: \"model_1\"\n",
      "INFO:absl:__________________________________________________________________________________________________\n",
      "INFO:absl: Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "INFO:absl:==================================================================================================\n",
      "INFO:absl: Age (InputLayer)               [(None, 1)]          0           []                               \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: EstimatedSalary (InputLayer)   [(None, 1)]          0           []                               \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: Gender (InputLayer)            [(None, 1)]          0           []                               \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: concatenate_1 (Concatenate)    (None, 3)            0           ['Age[0][0]',                    \n",
      "INFO:absl:                                                                  'EstimatedSalary[0][0]',        \n",
      "INFO:absl:                                                                  'Gender[0][0]']                 \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: dense_3 (Dense)                (None, 8)            32          ['concatenate_1[0][0]']          \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: dense_4 (Dense)                (None, 8)            72          ['dense_3[0][0]']                \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl: dense_5 (Dense)                (None, 1)            9           ['dense_4[0][0]']                \n",
      "INFO:absl:                                                                                                  \n",
      "INFO:absl:==================================================================================================\n",
      "INFO:absl:Total params: 113\n",
      "INFO:absl:Trainable params: 113\n",
      "INFO:absl:Non-trainable params: 0\n",
      "INFO:absl:__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "100/100 [==============================] - 1s 3ms/step - loss: 1322.7351 - binary_accuracy: 0.5400 - val_loss: 0.6327 - val_binary_accuracy: 0.7000\n",
      "Epoch 2/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6641 - binary_accuracy: 0.6209 - val_loss: 0.6337 - val_binary_accuracy: 0.6875\n",
      "Epoch 3/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6639 - binary_accuracy: 0.6206 - val_loss: 0.6256 - val_binary_accuracy: 0.7000\n",
      "Epoch 4/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6635 - binary_accuracy: 0.6212 - val_loss: 0.6249 - val_binary_accuracy: 0.7000\n",
      "Epoch 5/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6636 - binary_accuracy: 0.6209 - val_loss: 0.6153 - val_binary_accuracy: 0.7188\n",
      "Epoch 6/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6632 - binary_accuracy: 0.6219 - val_loss: 0.6279 - val_binary_accuracy: 0.6938\n",
      "Epoch 7/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6644 - binary_accuracy: 0.6194 - val_loss: 0.6247 - val_binary_accuracy: 0.7000\n",
      "Epoch 8/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6631 - binary_accuracy: 0.6222 - val_loss: 0.6309 - val_binary_accuracy: 0.6875\n",
      "Epoch 9/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6649 - binary_accuracy: 0.6184 - val_loss: 0.6249 - val_binary_accuracy: 0.7000\n",
      "Epoch 10/50\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6627 - binary_accuracy: 0.6228 - val_loss: 0.6307 - val_binary_accuracy: 0.6875\n",
      "Epoch 11/50\n",
      "100/100 [==============================] - 0s 4ms/step - loss: 0.6651 - binary_accuracy: 0.6181 - val_loss: 0.6432 - val_binary_accuracy: 0.6625\n",
      "Epoch 12/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6637 - binary_accuracy: 0.6209 - val_loss: 0.6371 - val_binary_accuracy: 0.6750\n",
      "Epoch 13/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6641 - binary_accuracy: 0.6200 - val_loss: 0.6218 - val_binary_accuracy: 0.7063\n",
      "Epoch 14/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6629 - binary_accuracy: 0.6225 - val_loss: 0.6158 - val_binary_accuracy: 0.7188\n",
      "Epoch 15/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6642 - binary_accuracy: 0.6200 - val_loss: 0.6275 - val_binary_accuracy: 0.6938\n",
      "Epoch 16/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6635 - binary_accuracy: 0.6212 - val_loss: 0.6335 - val_binary_accuracy: 0.6812\n",
      "Epoch 17/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6638 - binary_accuracy: 0.6206 - val_loss: 0.6311 - val_binary_accuracy: 0.6875\n",
      "Epoch 18/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6638 - binary_accuracy: 0.6206 - val_loss: 0.6184 - val_binary_accuracy: 0.7125\n",
      "Epoch 19/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6649 - binary_accuracy: 0.6184 - val_loss: 0.6254 - val_binary_accuracy: 0.7000\n",
      "Epoch 20/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6636 - binary_accuracy: 0.6212 - val_loss: 0.6159 - val_binary_accuracy: 0.7188\n",
      "Epoch 21/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6639 - binary_accuracy: 0.6206 - val_loss: 0.6371 - val_binary_accuracy: 0.6750\n",
      "Epoch 22/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6630 - binary_accuracy: 0.6225 - val_loss: 0.6280 - val_binary_accuracy: 0.6938\n",
      "Epoch 23/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6631 - binary_accuracy: 0.6222 - val_loss: 0.6301 - val_binary_accuracy: 0.6875\n",
      "Epoch 24/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6648 - binary_accuracy: 0.6187 - val_loss: 0.6278 - val_binary_accuracy: 0.6938\n",
      "Epoch 25/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6637 - binary_accuracy: 0.6209 - val_loss: 0.6275 - val_binary_accuracy: 0.6938\n",
      "Epoch 26/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6635 - binary_accuracy: 0.6212 - val_loss: 0.6306 - val_binary_accuracy: 0.6875\n",
      "Epoch 27/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6637 - binary_accuracy: 0.6209 - val_loss: 0.6236 - val_binary_accuracy: 0.7000\n",
      "Epoch 28/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6644 - binary_accuracy: 0.6197 - val_loss: 0.6165 - val_binary_accuracy: 0.7188\n",
      "Epoch 29/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6637 - binary_accuracy: 0.6209 - val_loss: 0.6311 - val_binary_accuracy: 0.6875\n",
      "Epoch 30/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6636 - binary_accuracy: 0.6212 - val_loss: 0.6140 - val_binary_accuracy: 0.7188\n",
      "Epoch 31/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6641 - binary_accuracy: 0.6203 - val_loss: 0.6247 - val_binary_accuracy: 0.7000\n",
      "Epoch 32/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6636 - binary_accuracy: 0.6212 - val_loss: 0.6221 - val_binary_accuracy: 0.7063\n",
      "Epoch 33/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6636 - binary_accuracy: 0.6212 - val_loss: 0.6431 - val_binary_accuracy: 0.6625\n",
      "Epoch 34/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6656 - binary_accuracy: 0.6172 - val_loss: 0.6155 - val_binary_accuracy: 0.7188\n",
      "Epoch 35/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6619 - binary_accuracy: 0.6247 - val_loss: 0.6310 - val_binary_accuracy: 0.6875\n",
      "Epoch 36/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6642 - binary_accuracy: 0.6200 - val_loss: 0.6339 - val_binary_accuracy: 0.6812\n",
      "Epoch 37/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6634 - binary_accuracy: 0.6216 - val_loss: 0.6181 - val_binary_accuracy: 0.7125\n",
      "Epoch 38/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6646 - binary_accuracy: 0.6191 - val_loss: 0.6279 - val_binary_accuracy: 0.6938\n",
      "Epoch 39/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6642 - binary_accuracy: 0.6200 - val_loss: 0.6130 - val_binary_accuracy: 0.7250\n",
      "Epoch 40/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6642 - binary_accuracy: 0.6200 - val_loss: 0.6463 - val_binary_accuracy: 0.6562\n",
      "Epoch 41/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6634 - binary_accuracy: 0.6216 - val_loss: 0.6187 - val_binary_accuracy: 0.7125\n",
      "Epoch 42/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6639 - binary_accuracy: 0.6206 - val_loss: 0.6191 - val_binary_accuracy: 0.7125\n",
      "Epoch 43/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6636 - binary_accuracy: 0.6212 - val_loss: 0.6247 - val_binary_accuracy: 0.7000\n",
      "Epoch 44/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6639 - binary_accuracy: 0.6206 - val_loss: 0.6338 - val_binary_accuracy: 0.6812\n",
      "Epoch 45/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6645 - binary_accuracy: 0.6194 - val_loss: 0.6184 - val_binary_accuracy: 0.7125\n",
      "Epoch 46/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6631 - binary_accuracy: 0.6222 - val_loss: 0.6276 - val_binary_accuracy: 0.6938\n",
      "Epoch 47/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6632 - binary_accuracy: 0.6219 - val_loss: 0.6145 - val_binary_accuracy: 0.7188\n",
      "Epoch 48/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6654 - binary_accuracy: 0.6175 - val_loss: 0.6253 - val_binary_accuracy: 0.7000\n",
      "Epoch 49/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6638 - binary_accuracy: 0.6206 - val_loss: 0.6250 - val_binary_accuracy: 0.7000\n",
      "Epoch 50/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6628 - binary_accuracy: 0.6228 - val_loss: 0.6088 - val_binary_accuracy: 0.7312\n",
      "INFO:tensorflow:Assets written to: pipelines/sample-pipeline/Trainer/model/7/Format-Serving/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: pipelines/sample-pipeline/Trainer/model/7/Format-Serving/assets\n",
      "INFO:absl:Training complete. Model written to pipelines/sample-pipeline/Trainer/model/7/Format-Serving. ModelRun written to pipelines/sample-pipeline/Trainer/model_run/7\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'>, {'model_run': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Trainer/model_run/7\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Trainer:model_run:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ModelRun\"\n",
      ")], 'model': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Trainer/model/7\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Trainer:model:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"Model\"\n",
      ")]}) for execution 7\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:51:10.862560  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component Trainer is finished.\n",
      "I0312 05:51:10.877475  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component Evaluator is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.evaluator.component.Evaluator\"\n",
      "  }\n",
      "  id: \"Evaluator\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.latest_blessed_model_resolver\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.Trainer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"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      \\\"per_slice_thresholds\\\": {\\n        \\\"binary_accuracy\\\": {\\n          \\\"thresholds\\\": [\\n            {\\n              \\\"slicing_specs\\\": [\\n                {}\\n              ],\\n              \\\"threshold\\\": {\\n                \\\"change_threshold\\\": {\\n                  \\\"absolute\\\": -1e-10,\\n                  \\\"direction\\\": \\\"HIGHER_IS_BETTER\\\"\\n                },\\n                \\\"value_threshold\\\": {\\n                  \\\"lower_bound\\\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \\\"model_specs\\\": [\\n    {\\n      \\\"label_key\\\": \\\"Purchased\\\",\\n      \\\"preprocessing_function_names\\\": [\\n        \\\"tft_layer\\\"\\n      ],\\n      \\\"signature_name\\\": \\\"serving_default\\\"\\n    }\\n  ],\\n  \\\"slicing_specs\\\": [\\n    {},\\n    {\\n      \\\"feature_keys\\\": [\\n        \\\"Gender\\\"\\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: \"Trainer\"\n",
      "upstream_nodes: \"latest_blessed_model_resolver\"\n",
      "downstream_nodes: \"Pusher\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:51:10.917862  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\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={'baseline_model': [], 'model': [Artifact(artifact: id: 14\n",
      "type_id: 28\n",
      "uri: \"pipelines/sample-pipeline/Trainer/model/7\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Trainer:model:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064270885\n",
      "last_update_time_since_epoch: 1647064270885\n",
      ", artifact_type: id: 28\n",
      "name: \"Model\"\n",
      ")], 'examples': [Artifact(artifact: id: 1\n",
      "type_id: 15\n",
      "uri: \"pipelines/sample-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:2,total_bytes:6956,xor_checksum:0,sum_checksum:3294124042\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:CsvExampleGen:examples: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: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064235861\n",
      "last_update_time_since_epoch: 1647064235861\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",
      ")]}, output_dict=defaultdict(<class 'list'>, {'blessing': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Evaluator/blessing/8\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Evaluator:blessing:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ModelBlessing\"\n",
      ")], 'evaluation': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Evaluator/evaluation/8\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Evaluator:evaluation:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ModelEvaluation\"\n",
      ")]}), exec_properties={'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"per_slice_thresholds\": {\\n        \"binary_accuracy\": {\\n          \"thresholds\": [\\n            {\\n              \"slicing_specs\": [\\n                {}\\n              ],\\n              \"threshold\": {\\n                \"change_threshold\": {\\n                  \"absolute\": -1e-10,\\n                  \"direction\": \"HIGHER_IS_BETTER\"\\n                },\\n                \"value_threshold\": {\\n                  \"lower_bound\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"Purchased\",\\n      \"preprocessing_function_names\": [\\n        \"tft_layer\"\\n      ],\\n      \"signature_name\": \"serving_default\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"Gender\"\\n      ]\\n    }\\n  ]\\n}', 'fairness_indicator_thresholds': 'null', 'example_splits': 'null'}, execution_output_uri='pipelines/sample-pipeline/Evaluator/.system/executor_execution/8/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/Evaluator/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/Evaluator/.system/executor_execution/8/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.evaluator.component.Evaluator\"\n",
      "  }\n",
      "  id: \"Evaluator\"\n",
      "}\n",
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      "  contexts {\n",
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      "      }\n",
      "    }\n",
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      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
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      "  }\n",
      "  contexts {\n",
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      "      name: \"node\"\n",
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      "      field_value {\n",
      "        string_value: \"sample-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",
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      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
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      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.latest_blessed_model_resolver\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.CsvExampleGen\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"Examples\"\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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.Trainer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"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      \\\"per_slice_thresholds\\\": {\\n        \\\"binary_accuracy\\\": {\\n          \\\"thresholds\\\": [\\n            {\\n              \\\"slicing_specs\\\": [\\n                {}\\n              ],\\n              \\\"threshold\\\": {\\n                \\\"change_threshold\\\": {\\n                  \\\"absolute\\\": -1e-10,\\n                  \\\"direction\\\": \\\"HIGHER_IS_BETTER\\\"\\n                },\\n                \\\"value_threshold\\\": {\\n                  \\\"lower_bound\\\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \\\"model_specs\\\": [\\n    {\\n      \\\"label_key\\\": \\\"Purchased\\\",\\n      \\\"preprocessing_function_names\\\": [\\n        \\\"tft_layer\\\"\\n      ],\\n      \\\"signature_name\\\": \\\"serving_default\\\"\\n    }\\n  ],\\n  \\\"slicing_specs\\\": [\\n    {},\\n    {\\n      \\\"feature_keys\\\": [\\n        \\\"Gender\\\"\\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: \"Trainer\"\n",
      "upstream_nodes: \"latest_blessed_model_resolver\"\n",
      "downstream_nodes: \"Pusher\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:udf_utils.get_fn {'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"per_slice_thresholds\": {\\n        \"binary_accuracy\": {\\n          \"thresholds\": [\\n            {\\n              \"slicing_specs\": [\\n                {}\\n              ],\\n              \"threshold\": {\\n                \"change_threshold\": {\\n                  \"absolute\": -1e-10,\\n                  \"direction\": \"HIGHER_IS_BETTER\"\\n                },\\n                \"value_threshold\": {\\n                  \"lower_bound\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"Purchased\",\\n      \"preprocessing_function_names\": [\\n        \"tft_layer\"\\n      ],\\n      \"signature_name\": \"serving_default\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"Gender\"\\n      ]\\n    }\\n  ]\\n}', 'fairness_indicator_thresholds': 'null', 'example_splits': 'null'} '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",
      "  signature_name: \"serving_default\"\n",
      "  label_key: \"Purchased\"\n",
      "  preprocessing_function_names: \"tft_layer\"\n",
      "}\n",
      "slicing_specs {\n",
      "}\n",
      "slicing_specs {\n",
      "  feature_keys: \"Gender\"\n",
      "}\n",
      "metrics_specs {\n",
      "  per_slice_thresholds {\n",
      "    key: \"binary_accuracy\"\n",
      "    value {\n",
      "      thresholds {\n",
      "        slicing_specs {\n",
      "        }\n",
      "        threshold {\n",
      "          value_threshold {\n",
      "            lower_bound {\n",
      "              value: 0.7\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:Using pipelines/sample-pipeline/Trainer/model/7/Format-Serving as  model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa91d509910> and <keras.engine.input_layer.InputLayer object at 0x7fa8dd1f0910>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa91d509910> and <keras.engine.input_layer.InputLayer object at 0x7fa8dd1f0910>).\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 {'eval_config': '{\\n  \"metrics_specs\": [\\n    {\\n      \"per_slice_thresholds\": {\\n        \"binary_accuracy\": {\\n          \"thresholds\": [\\n            {\\n              \"slicing_specs\": [\\n                {}\\n              ],\\n              \"threshold\": {\\n                \"change_threshold\": {\\n                  \"absolute\": -1e-10,\\n                  \"direction\": \"HIGHER_IS_BETTER\"\\n                },\\n                \"value_threshold\": {\\n                  \"lower_bound\": 0.7\\n                }\\n              }\\n            }\\n          ]\\n        }\\n      }\\n    }\\n  ],\\n  \"model_specs\": [\\n    {\\n      \"label_key\": \"Purchased\",\\n      \"preprocessing_function_names\": [\\n        \"tft_layer\"\\n      ],\\n      \"signature_name\": \"serving_default\"\\n    }\\n  ],\\n  \"slicing_specs\": [\\n    {},\\n    {\\n      \"feature_keys\": [\\n        \"Gender\"\\n      ]\\n    }\\n  ]\\n}', 'fairness_indicator_thresholds': 'null', 'example_splits': 'null'} '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",
      "  signature_name: \"serving_default\"\n",
      "  label_key: \"Purchased\"\n",
      "  preprocessing_function_names: \"tft_layer\"\n",
      "}\n",
      "slicing_specs {\n",
      "}\n",
      "slicing_specs {\n",
      "  feature_keys: \"Gender\"\n",
      "}\n",
      "metrics_specs {\n",
      "  model_names: \"\"\n",
      "  per_slice_thresholds {\n",
      "    key: \"binary_accuracy\"\n",
      "    value {\n",
      "      thresholds {\n",
      "        slicing_specs {\n",
      "        }\n",
      "        threshold {\n",
      "          value_threshold {\n",
      "            lower_bound {\n",
      "              value: 0.7\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\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",
      "  signature_name: \"serving_default\"\n",
      "  label_key: \"Purchased\"\n",
      "  preprocessing_function_names: \"tft_layer\"\n",
      "}\n",
      "slicing_specs {\n",
      "}\n",
      "slicing_specs {\n",
      "  feature_keys: \"Gender\"\n",
      "}\n",
      "metrics_specs {\n",
      "  model_names: \"\"\n",
      "  per_slice_thresholds {\n",
      "    key: \"binary_accuracy\"\n",
      "    value {\n",
      "      thresholds {\n",
      "        slicing_specs {\n",
      "        }\n",
      "        threshold {\n",
      "          value_threshold {\n",
      "            lower_bound {\n",
      "              value: 0.7\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\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",
      "  signature_name: \"serving_default\"\n",
      "  label_key: \"Purchased\"\n",
      "  preprocessing_function_names: \"tft_layer\"\n",
      "}\n",
      "slicing_specs {\n",
      "}\n",
      "slicing_specs {\n",
      "  feature_keys: \"Gender\"\n",
      "}\n",
      "metrics_specs {\n",
      "  model_names: \"\"\n",
      "  per_slice_thresholds {\n",
      "    key: \"binary_accuracy\"\n",
      "    value {\n",
      "      thresholds {\n",
      "        slicing_specs {\n",
      "        }\n",
      "        threshold {\n",
      "          value_threshold {\n",
      "            lower_bound {\n",
      "              value: 0.7\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa94fa90510> and <keras.engine.input_layer.InputLayer object at 0x7fa94fb47ed0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa94fa90510> and <keras.engine.input_layer.InputLayer object at 0x7fa94fb47ed0>).\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa94ffe1c90> and <keras.engine.input_layer.InputLayer object at 0x7fa9bc8f6690>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa94ffe1c90> and <keras.engine.input_layer.InputLayer object at 0x7fa9bc8f6690>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8fbe51610> and <keras.engine.input_layer.InputLayer object at 0x7fa8fbe772d0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8fbe51610> and <keras.engine.input_layer.InputLayer object at 0x7fa8fbe772d0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bce0cf50> and <keras.engine.input_layer.InputLayer object at 0x7fa91da15f50>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa9bce0cf50> and <keras.engine.input_layer.InputLayer object at 0x7fa91da15f50>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8fbec1850> and <keras.engine.input_layer.InputLayer object at 0x7fa8fbef09d0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8fbec1850> and <keras.engine.input_layer.InputLayer object at 0x7fa8fbef09d0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8f80c7a10> and <keras.engine.input_layer.InputLayer object at 0x7fa8f80d5990>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8f80c7a10> and <keras.engine.input_layer.InputLayer object at 0x7fa8f80d5990>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8fbf32ed0> and <keras.engine.input_layer.InputLayer object at 0x7fa8fbf014d0>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8fbf32ed0> and <keras.engine.input_layer.InputLayer object at 0x7fa8fbf014d0>).\n",
      "INFO:absl:Evaluation complete. Results written to pipelines/sample-pipeline/Evaluator/evaluation/8.\n",
      "INFO:absl:Checking validation results.\n",
      "INFO:absl:Blessing result False written to pipelines/sample-pipeline/Evaluator/blessing/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'>, {'blessing': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Evaluator/blessing/8\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Evaluator:blessing:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ModelBlessing\"\n",
      ")], 'evaluation': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Evaluator/evaluation/8\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Evaluator:evaluation:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"ModelEvaluation\"\n",
      ")]}) for execution 8\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:51:16.756448  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "I0312 05:51:16.772370  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component Evaluator is finished.\n",
      "INFO:absl:Component Pusher is running.\n",
      "INFO:absl:Running launcher for node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.pusher.component.Pusher\"\n",
      "  }\n",
      "  id: \"Pusher\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.Trainer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-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",
      "        }\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\\\": \\\"model\\\"\\n  }\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"Evaluator\"\n",
      "upstream_nodes: \"Trainer\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      "\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:51:16.807895  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\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={'model_blessing': [Artifact(artifact: id: 15\n",
      "type_id: 30\n",
      "uri: \"pipelines/sample-pipeline/Evaluator/blessing/8\"\n",
      "custom_properties {\n",
      "  key: \"blessed\"\n",
      "  value {\n",
      "    int_value: 0\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"current_model\"\n",
      "  value {\n",
      "    string_value: \"pipelines/sample-pipeline/Trainer/model/7\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"current_model_id\"\n",
      "  value {\n",
      "    int_value: 14\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Evaluator:blessing:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064276779\n",
      "last_update_time_since_epoch: 1647064276779\n",
      ", artifact_type: id: 30\n",
      "name: \"ModelBlessing\"\n",
      ")], 'model': [Artifact(artifact: id: 14\n",
      "type_id: 28\n",
      "uri: \"pipelines/sample-pipeline/Trainer/model/7\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Trainer:model:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      "state: LIVE\n",
      "create_time_since_epoch: 1647064270885\n",
      "last_update_time_since_epoch: 1647064270885\n",
      ", artifact_type: id: 28\n",
      "name: \"Model\"\n",
      ")]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Pusher/pushed_model/9\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Pusher:pushed_model:0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"PushedModel\"\n",
      ")]}), exec_properties={'push_destination': '{\\n  \"filesystem\": {\\n    \"base_directory\": \"model\"\\n  }\\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/sample-pipeline/Pusher/.system/executor_execution/9/executor_output.pb', stateful_working_dir='pipelines/sample-pipeline/Pusher/.system/stateful_working_dir/2022-03-12T05:50:34.538287', tmp_dir='pipelines/sample-pipeline/Pusher/.system/executor_execution/9/.temp/', pipeline_node=node_info {\n",
      "  type {\n",
      "    name: \"tfx.components.pusher.component.Pusher\"\n",
      "  }\n",
      "  id: \"Pusher\"\n",
      "}\n",
      "contexts {\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-pipeline\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"pipeline_run\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"2022-03-12T05:50:34.538287\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  contexts {\n",
      "    type {\n",
      "      name: \"node\"\n",
      "    }\n",
      "    name {\n",
      "      field_value {\n",
      "        string_value: \"sample-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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-pipeline.Trainer\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        artifact_query {\n",
      "          type {\n",
      "            name: \"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: \"sample-pipeline\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"pipeline_run\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"2022-03-12T05:50:34.538287\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "        context_queries {\n",
      "          type {\n",
      "            name: \"node\"\n",
      "          }\n",
      "          name {\n",
      "            field_value {\n",
      "              string_value: \"sample-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",
      "        }\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\\\": \\\"model\\\"\\n  }\\n}\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "upstream_nodes: \"Evaluator\"\n",
      "upstream_nodes: \"Trainer\"\n",
      "execution_options {\n",
      "  caching_options {\n",
      "  }\n",
      "}\n",
      ", pipeline_info=id: \"sample-pipeline\"\n",
      ", pipeline_run_id='2022-03-12T05:50:34.538287')\n",
      "INFO:absl:Model on pipelines/sample-pipeline/Evaluator/blessing/8 was not blessed by model validation\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'>, {'pushed_model': [Artifact(artifact: uri: \"pipelines/sample-pipeline/Pusher/pushed_model/9\"\n",
      "custom_properties {\n",
      "  key: \"name\"\n",
      "  value {\n",
      "    string_value: \"sample-pipeline:2022-03-12T05:50:34.538287:Pusher:pushed_model:0\"\n",
      "  }\n",
      "}\n",
      "custom_properties {\n",
      "  key: \"tfx_version\"\n",
      "  value {\n",
      "    string_value: \"1.4.0\"\n",
      "  }\n",
      "}\n",
      ", artifact_type: name: \"PushedModel\"\n",
      ")]}) for execution 9\n",
      "INFO:absl:MetadataStore with DB connection initialized\n",
      "I0312 05:51:16.850045  3384 rdbms_metadata_access_object.cc:686] No property is defined for the Type\n",
      "INFO:absl:Component Pusher is finished.\n"
     ]
    }
   ],
   "source": [
    "tfx.orchestration.LocalDagRunner().run(\n",
    "    create_pipeline(\n",
    "        pipeline_name=PIPELINE_NAME,\n",
    "        pipeline_directory=str(PIPELINE_DIRECTORY),\n",
    "        data_directory=DATA_DIRECTORY,\n",
    "        schema_path=str(SCHEMA_DIRECTORY),\n",
    "        model_directory=str(MODEL_DIRECTORY),\n",
    "        metadata_path=str(METADATA_PATH),\n",
    "        transform_module_path=TRANSFORM_MODULE_PATH,\n",
    "        trainer_module_path=TRAINER_MODULE_PATH\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f703cbdc-a5e1-4348-9185-9c92991f9d74",
   "metadata": {},
   "source": [
    "### Running the Inferencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b5f31aad-7f5c-4669-be7c-aaec4015a45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8c2e4f250> and <keras.engine.input_layer.InputLayer object at 0x7fa8c2e92d10>).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fa8c2e4f250> and <keras.engine.input_layer.InputLayer object at 0x7fa8c2e92d10>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.3264355 ]\n",
      " [0.97265285]\n",
      " [0.6270244 ]\n",
      " [0.44954094]\n",
      " [0.6055691 ]\n",
      " [0.43268377]\n",
      " [0.9931779 ]\n",
      " [0.9590658 ]\n",
      " [0.9381055 ]\n",
      " [0.16483685]\n",
      " [0.00963226]\n",
      " [0.17854801]\n",
      " [0.5440971 ]\n",
      " [0.12227309]\n",
      " [0.20295498]\n",
      " [0.99399805]\n",
      " [0.31204042]\n",
      " [0.779172  ]\n",
      " [0.4713493 ]\n",
      " [0.06559923]\n",
      " [0.99009776]\n",
      " [0.98698556]\n",
      " [0.06094879]\n",
      " [0.60351074]\n",
      " [0.07195705]\n",
      " [0.6666118 ]\n",
      " [0.92699564]\n",
      " [0.9809886 ]\n",
      " [0.25242084]\n",
      " [0.5700159 ]\n",
      " [0.28742206]\n",
      " [0.30117548]\n",
      " [0.11593023]\n",
      " [0.23361868]\n",
      " [0.44971702]\n",
      " [0.167728  ]\n",
      " [0.8054378 ]\n",
      " [0.18791357]\n",
      " [0.17147547]\n",
      " [0.11504939]\n",
      " [0.04695523]\n",
      " [0.99569154]\n",
      " [0.8914021 ]\n",
      " [0.99695516]\n",
      " [0.01810947]\n",
      " [0.9591758 ]\n",
      " [0.55371517]\n",
      " [0.06376794]\n",
      " [0.17280924]\n",
      " [0.05554444]\n",
      " [0.46687847]\n",
      " [0.0558475 ]\n",
      " [0.18377075]\n",
      " [0.24303532]\n",
      " [0.9293905 ]\n",
      " [0.19258127]\n",
      " [0.8423981 ]\n",
      " [0.0571115 ]\n",
      " [0.09974855]\n",
      " [0.00706607]\n",
      " [0.32770076]\n",
      " [0.13857907]\n",
      " [0.9964013 ]\n",
      " [0.33311385]\n",
      " [0.49687067]\n",
      " [0.7889198 ]\n",
      " [0.18616706]\n",
      " [0.93134916]\n",
      " [0.6897022 ]\n",
      " [0.23582542]\n",
      " [0.59192103]\n",
      " [0.89851034]\n",
      " [0.9869486 ]\n",
      " [0.51845723]\n",
      " [0.7017367 ]\n",
      " [0.9965995 ]\n",
      " [0.632553  ]\n",
      " [0.88693655]\n",
      " [0.40275878]\n",
      " [0.00451452]\n",
      " [0.9087225 ]\n",
      " [0.20342189]\n",
      " [0.9832604 ]\n",
      " [0.92737937]\n",
      " [0.2992049 ]\n",
      " [0.11593023]\n",
      " [0.30957186]\n",
      " [0.8826796 ]\n",
      " [0.02553624]\n",
      " [0.30573913]\n",
      " [0.16812724]\n",
      " [0.05259797]\n",
      " [0.5299444 ]\n",
      " [0.13358647]\n",
      " [0.14004865]\n",
      " [0.59849703]\n",
      " [0.7322469 ]\n",
      " [0.92511976]\n",
      " [0.22522795]\n",
      " [0.00535342]\n",
      " [0.4647228 ]\n",
      " [0.26694855]\n",
      " [0.23699781]\n",
      " [0.9962913 ]\n",
      " [0.82442254]\n",
      " [0.17280924]\n",
      " [0.02429476]\n",
      " [0.23751694]\n",
      " [0.99875784]\n",
      " [0.3963827 ]\n",
      " [0.34928963]\n",
      " [0.9926255 ]\n",
      " [0.55371517]\n",
      " [0.9904445 ]\n",
      " [0.99217534]\n",
      " [0.44954094]\n",
      " [0.1940291 ]\n",
      " [0.05142778]\n",
      " [0.21388224]\n",
      " [0.28742206]]\n"
     ]
    }
   ],
   "source": [
    "inference_fn = get_inference_fn(MODEL_DIRECTORY)\n",
    "result = inference_fn(examples=tf.constant(_examples(test_df)))\n",
    "print(result[\"output_0\"].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f9549264-173a-41c8-820f-7aa600ff408a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: ..: command not found\n"
     ]
    }
   ],
   "source": [
    "!.. ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c528785-0905-43c1-8f3e-00d4bba81645",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting zip\n",
      "  Downloading zip-0.0.2.tar.gz (3.0 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting unzip\n",
      "  Downloading unzip-1.0.0.tar.gz (704 bytes)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting Flask-Admin>=1.0.4\n",
      "  Downloading Flask-Admin-1.6.0.tar.gz (6.6 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.6/6.6 MB\u001b[0m \u001b[31m57.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting Flask-Bootstrap>=2.2.2-1\n",
      "  Downloading Flask-Bootstrap-3.3.7.1.tar.gz (456 kB)\n",
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      "  Preparing metadata (setup.py) ... \u001b[?25lerror\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m \u001b[31m[9 lines of output]\u001b[0m\n",
      "  \u001b[31m   \u001b[0m Traceback (most recent call last):\n",
      "  \u001b[31m   \u001b[0m   File \"<string>\", line 36, in <module>\n",
      "  \u001b[31m   \u001b[0m   File \"<pip-setuptools-caller>\", line 34, in <module>\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-0agj5q5w/wsgiref_195a8903c6b54a4c951ea8d299aa7d07/setup.py\", line 5, in <module>\n",
      "  \u001b[31m   \u001b[0m     import ez_setup\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-0agj5q5w/wsgiref_195a8903c6b54a4c951ea8d299aa7d07/ez_setup/__init__.py\", line 170\n",
      "  \u001b[31m   \u001b[0m     print \"Setuptools version\",version,\"or greater has been installed.\"\n",
      "  \u001b[31m   \u001b[0m                              ^\n",
      "  \u001b[31m   \u001b[0m SyntaxError: Missing parentheses in call to 'print'. Did you mean print(\"Setuptools version\",version,\"or greater has been installed.\")?\n",
      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
      "\n",
      "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
      "\u001b[31m╰─>\u001b[0m See above for output.\n",
      "\n",
      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
      "\u001b[1;36mhint\u001b[0m: See above for details.\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!zip -r filename.zip folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2dfaaa4-a9f1-4711-a551-829f6fd79d3b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "python3",
   "name": "tf2-gpu.2-6.m90",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m90"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}