{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"2022-01-25-mlops-tfx.ipynb","provenance":[{"file_id":"https://github.com/recohut/nbs/blob/main/raw/T328749%20%7C%20MLOps%20with%20TFX%20Pipeline.ipynb","timestamp":1644671164117},{"file_id":"https://gist.github.com/rafiqhasan/2164304ede002f4a8bfe56e5434e1a34#file-dl-e2e-taxi-dataset-tfx-e2e-ipynb","timestamp":1628656487313}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","source":["# MLOps with TFX Pipeline"],"metadata":{"id":"DBkcVEByCra5"}},{"cell_type":"markdown","metadata":{"id":"DUH2FIZH73M_"},"source":["Author - [Hasan Rafiq](https://www.linkedin.com/in/sam04/)"]},{"cell_type":"markdown","metadata":{"id":"MZOYTt1RW4TK"},"source":["\n","## **TensorFlow Extended (TFX)** \n","Is an end-to-end platform for deploying production ML pipelines"]},{"cell_type":"markdown","metadata":{"id":"NPI8kU6ClvGe"},"source":["![prog_fin.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"PemmMleYAtXX"},"source":["### **Enterprise ML is not about the best model:**\n","![mlops-continuous-delivery-and-automation-pipelines-in-machine-learning-1-elements-of-ml.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"BYwKL3V4n1K5"},"source":["### **Corresponding TFX libraries per component**:\n","\n","![libraries_components.png](data:image/png;base64,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)"]},{"cell_type":"code","metadata":{"id":"J9XeXakTLkZ5"},"source":["!pip install tfx"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YIqpWK9efviJ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"97beba54-58c2-4cef-8e13-7dcb0d1b7e10"},"source":["import os\n","import pprint\n","import numpy as np\n","import tempfile\n","import urllib\n","\n","import absl\n","import pandas as pd\n","import tensorflow as tf\n","import tensorflow_model_analysis as tfma\n","tf.get_logger().propagate = False\n","pp = pprint.PrettyPrinter()\n","\n","import tfx\n","from tfx.components import CsvExampleGen\n","from typing import Dict, List, Text\n","from tfx.components import Evaluator\n","from tfx.components import ExampleValidator\n","from tfx.components import Pusher\n","from tfx.components import ResolverNode\n","from tfx.components import SchemaGen\n","from tfx.components import StatisticsGen\n","from tfx.components import Trainer\n","from tfx.components import Transform\n","from tfx.components.base import executor_spec\n","from tfx.components.trainer.executor import GenericExecutor\n","from tfx.dsl.experimental import latest_blessed_model_resolver\n","from tfx.orchestration import metadata\n","from tfx.orchestration import pipeline\n","from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext\n","from tfx.proto import pusher_pb2\n","from tfx.proto import trainer_pb2\n","from tfx.types import Channel\n","from tfx.types.standard_artifacts import Model\n","from tfx.types.standard_artifacts import ModelBlessing\n","from tfx.utils.dsl_utils import external_input\n","\n","\n","%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"wCZTHRy0N1D6"},"source":["Let's check the library versions."]},{"cell_type":"code","metadata":{"id":"eZ4K18_DN2D8","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f5b29d20-33dc-4d9c-eb59-ca903cbee1f1"},"source":["print('TensorFlow version: {}'.format(tf.__version__))\n","print('TFX version: {}'.format(tfx.__version__))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["TensorFlow version: 2.4.1\n","TFX version: 0.28.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"grFKIgyXOAVM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"79010cb6-21d5-417e-de56-f59922a57c19"},"source":["!rm -rf data.*\n","!rm -rf *trainer.py\n","!sudo rm -r /content/tfx"],"execution_count":null,"outputs":[{"output_type":"stream","text":["rm: cannot remove '/content/tfx': No such file or directory\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"r_17y324BB0K"},"source":["! cd /content/\n","! mkdir /content/tfx/\n","! mkdir /content/tfx/pipelines\n","! mkdir /content/tfx/metadata\n","! mkdir /content/tfx/logs\n","! mkdir /content/tfx/data\n","! mkdir /content/tfx/serving_model"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"n2cMMAbSkGfX"},"source":["### Download example data\n","We download the example dataset for use in our TFX pipeline.\n","\n","The dataset we're using is the [Taxi Trips dataset](https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew) released by the City of Chicago. The columns in this dataset are:\n","\n","<table>\n","<tr><td>pickup_community_area</td><td>fare</td><td>trip_start_month</td></tr>\n","<tr><td>trip_start_hour</td><td>trip_start_day</td><td>trip_start_timestamp</td></tr>\n","<tr><td>pickup_latitude</td><td>pickup_longitude</td><td>dropoff_latitude</td></tr>\n","<tr><td>dropoff_longitude</td><td>trip_miles</td><td>pickup_census_tract</td></tr>\n","<tr><td>dropoff_census_tract</td><td>payment_type</td><td>company</td></tr>\n","<tr><td>trip_seconds</td><td>dropoff_community_area</td><td>tips</td></tr>\n","</table>\n","\n","With this dataset, we will build a model that predicts the `fare` of a trip."]},{"cell_type":"code","metadata":{"id":"BywX6OUEhAqn","colab":{"base_uri":"https://localhost:8080/"},"outputId":"ce71f8dc-7d2d-49ca-f6ab-f181f368e780"},"source":["!wget https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-03-27 14:45:42--  https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1922812 (1.8M) [text/plain]\n","Saving to: ‘data.csv’\n","\n","data.csv            100%[===================>]   1.83M  --.-KB/s    in 0.08s   \n","\n","2021-03-27 14:45:43 (23.8 MB/s) - ‘data.csv’ saved [1922812/1922812]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SakyMaydJ4G5","colab":{"base_uri":"https://localhost:8080/"},"outputId":"adc143eb-9e40-4205-c9ba-bc1fa8d1d13d"},"source":["df = pd.read_csv('/content/data.csv')\n","\n","##Drop useless columns\n","df = df.drop(['trip_start_timestamp','trip_miles','pickup_census_tract',\n","              'dropoff_census_tract','trip_seconds','payment_type','tips', \n","              'company','dropoff_community_area','pickup_community_area'], axis=1)\n","\n","#Drop NA rows\n","df = df.dropna()\n","\n","##Keep a test set for final testing( TFX internally splits train and validation data )\n","np.random.seed(seed=2)\n","msk = np.random.rand(len(df)) < 0.9\n","traindf = df[msk]\n","evaldf = df[~msk]\n","\n","print(len(traindf))\n","print(len(evaldf))\n","\n","traindf.to_csv(\"/content/tfx/data/data_trans.csv\", index=False, header=True)\n","evaldf.to_csv(\"eval.csv\", index=False, header=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["13077\n","1442\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"blZC1sIQOWfH"},"source":["Take a quick look at the CSV file."]},{"cell_type":"code","metadata":{"id":"c5YPeLPFOXaD","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c6fe1a10-47fa-4b37-97af-55165f11c16b"},"source":["!head {_data_filepath}"],"execution_count":null,"outputs":[{"output_type":"stream","text":["head: cannot open '{_data_filepath}' for reading: No such file or directory\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ufJKQ6OvkJlY"},"source":["### Set up pipeline paths"]},{"cell_type":"code","metadata":{"id":"RsH0ckYiADx5"},"source":["##Define all constant\n","_tfx_root = os.path.join(os.getcwd(), 'tfx');        # Create location ~/tfx\n","_pipeline_root = os.path.join(_tfx_root, 'pipelines');      # Join ~/tfx/pipelines/\n","_metadata_db_root = os.path.join(_tfx_root, 'metadata.db');    # Join ~/tfx/metadata.db\n","_log_root = os.path.join(_tfx_root, 'logs');\n","_model_root = os.path.join(_tfx_root, 'model');\n","_data_root = os.path.join(_tfx_root, 'data');\n","_serving_model_dir = os.path.join(_tfx_root, 'serving_model')\n","_data_filepath = os.path.join(_data_root, \"data_trans.csv\")\n","\n","_input_fn_module_file = 'inputfn_trainer.py'\n","_constants_module_file = 'constants_trainer.py'\n","_model_trainer_module_file = 'model_trainer.py'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8ONIE_hdkPS4"},"source":["### Create the InteractiveContext\n","Last, we create an InteractiveContext, which will allow us to run TFX components interactively in this notebook."]},{"cell_type":"code","metadata":{"id":"0Rh6K5sUf9dd","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5591a3d5-6c64-4d58-d26b-b607b5e9b0c9"},"source":["# Here, we create an InteractiveContext using default parameters. This will\n","# use a temporary directory with an ephemeral ML Metadata database instance.\n","# To use your own pipeline root or database, the optional properties\n","# `pipeline_root` and `metadata_connection_config` may be passed to\n","# InteractiveContext. Calls to InteractiveContext are no-ops outside of the\n","# notebook.\n","context = InteractiveContext(pipeline_root=_tfx_root)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /content/tfx/metadata.sqlite.\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"HdQWxfsVkzdJ"},"source":["## Run TFX components interactively\n","In the cells that follow, we create TFX components one-by-one, run each of them, and visualize their output artifacts."]},{"cell_type":"markdown","metadata":{"id":"L9fwt9gQk3BR"},"source":["### ExampleGen\n","\n","The `ExampleGen` component is usually at the start of a TFX pipeline. It will:\n","\n","1.   **Split** data( placed in _data_root ) into training and evaluation sets (by default, 2/3 training + 1/3 eval)\n","2.   Convert data into the `tf.Example` format\n","3.   **Copy splits** into the `_tfx_root` directory for other components to access\n","\n","`ExampleGen` takes as input the path to your data source. In our case, this is the `_data_root` path that contains the downloaded CSV.\n","\n","Note: In this notebook, we can instantiate components one-by-one and run them with `InteractiveContext.run()`. By contrast, in a production setting, we would specify all the components upfront in a `Pipeline` to pass to the orchestrator (see the [Building a TFX Pipeline Guide](../tfx/guide/build_tfx_pipeline))."]},{"cell_type":"code","metadata":{"id":"PyXjuMt8f-9u","colab":{"base_uri":"https://localhost:8080/","height":495},"outputId":"ee240eaf-dd4e-40c1-c381-1e899bf16b36"},"source":["example_gen = CsvExampleGen(input=external_input(_data_root))\n","context.run(example_gen)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:absl:From <ipython-input-10-2e0190c2dd16>:1: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","external_input is deprecated, directly pass the uri to ExampleGen.\n","WARNING:absl:The \"input\" argument to the CsvExampleGen component has been deprecated by \"input_base\". Please update your usage as support for this argument will be removed soon.\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"],"name":"stderr"},{"output_type":"display_data","data":{"application/javascript":["\n","        if (typeof window.interactive_beam_jquery == 'undefined') {\n","          var jqueryScript = document.createElement('script');\n","          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n","          jqueryScript.type = 'text/javascript';\n","          jqueryScript.onload = function() {\n","            var datatableScript = document.createElement('script');\n","            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n","            datatableScript.type = 'text/javascript';\n","            datatableScript.onload = function() {\n","              window.interactive_beam_jquery = jQuery.noConflict(true);\n","              window.interactive_beam_jquery(document).ready(function($){\n","                \n","              });\n","            }\n","            document.head.appendChild(datatableScript);\n","          };\n","          document.head.appendChild(jqueryScript);\n","        } else {\n","          window.interactive_beam_jquery(document).ready(function($){\n","            \n","          });\n","        }"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["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"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["<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 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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 0x7f97bc88c8d0</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: 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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 0x7f97bc88c8d0</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: /content/tfx/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7f9767e35f50</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\">/content/tfx/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: 5\n","        uri: \"/content/tfx/CsvExampleGen/examples/1\"\n","        properties {\n","          key: \"split_names\"\n","          value {\n","            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"input_fingerprint\"\n","          value {\n","            string_value: \"split:single_split,num_files:1,total_bytes:907007,xor_checksum:1616856396,sum_checksum:1616856396\"\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","            string_value: \"0\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"state\"\n","          value {\n","            string_value: \"published\"\n","          }\n","        }\n","        state: LIVE\n","        , artifact_type: id: 5\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","        ))"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"OqCoZh7KPUm9"},"source":["Let's examine the output artifacts of `ExampleGen`. This component produces two artifacts, training examples and evaluation examples:"]},{"cell_type":"code","metadata":{"id":"880KkTAkPeUg","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9dbd3b1e-4e13-44c9-e062-8eb1e5b5b799"},"source":["artifact = example_gen.outputs['examples'].get()[0]\n","print(artifact.split_names, artifact.uri)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[\"train\", \"eval\"] /content/tfx/CsvExampleGen/examples/1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"J6vcbW_wPqvl"},"source":["We can also take a look at the first three training examples:"]},{"cell_type":"code","metadata":{"id":"H4XIXjiCPwzQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f55974d3-9a4b-43b3-b236-e1877625bd7e"},"source":["# Get the URI of the output artifact representing the training examples, which is a directory\n","train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'train')\n","\n","# Get the list of files in this directory (all compressed TFRecord files)\n","tfrecord_filenames = [os.path.join(train_uri, name)\n","                      for name in os.listdir(train_uri)]\n","\n","# Create a `TFRecordDataset` to read these files\n","dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")\n","\n","# Iterate over the first 3 records and decode them.\n","for tfrecord in dataset.take(3):\n","  serialized_example = tfrecord.numpy()\n","  example = tf.train.Example()\n","  example.ParseFromString(serialized_example)\n","  pp.pprint(example)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["features {\n","  feature {\n","    key: \"dropoff_latitude\"\n","    value {\n","      float_list {\n","        value: 41.92045211791992\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"dropoff_longitude\"\n","    value {\n","      float_list {\n","        value: -87.6799545288086\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"fare\"\n","    value {\n","      float_list {\n","        value: 3.8499999046325684\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_latitude\"\n","    value {\n","      float_list {\n","        value: 41.8996696472168\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_longitude\"\n","    value {\n","      float_list {\n","        value: -87.66983795166016\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_day\"\n","    value {\n","      int64_list {\n","        value: 6\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_hour\"\n","    value {\n","      int64_list {\n","        value: 15\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_month\"\n","    value {\n","      int64_list {\n","        value: 3\n","      }\n","    }\n","  }\n","}\n","\n","features {\n","  feature {\n","    key: \"dropoff_latitude\"\n","    value {\n","      float_list {\n","        value: 41.92045211791992\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"dropoff_longitude\"\n","    value {\n","      float_list {\n","        value: -87.6799545288086\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"fare\"\n","    value {\n","      float_list {\n","        value: 7.25\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_latitude\"\n","    value {\n","      float_list {\n","        value: 41.90665054321289\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_longitude\"\n","    value {\n","      float_list {\n","        value: -87.66533660888672\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_day\"\n","    value {\n","      int64_list {\n","        value: 7\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_hour\"\n","    value {\n","      int64_list {\n","        value: 21\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_month\"\n","    value {\n","      int64_list {\n","        value: 10\n","      }\n","    }\n","  }\n","}\n","\n","features {\n","  feature {\n","    key: \"dropoff_latitude\"\n","    value {\n","      float_list {\n","        value: 41.849246978759766\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"dropoff_longitude\"\n","    value {\n","      float_list {\n","        value: -87.62413787841797\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"fare\"\n","    value {\n","      float_list {\n","        value: 13.050000190734863\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_latitude\"\n","    value {\n","      float_list {\n","        value: 41.849246978759766\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_longitude\"\n","    value {\n","      float_list {\n","        value: -87.62413787841797\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_day\"\n","    value {\n","      int64_list {\n","        value: 2\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_hour\"\n","    value {\n","      int64_list {\n","        value: 17\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_month\"\n","    value {\n","      int64_list {\n","        value: 9\n","      }\n","    }\n","  }\n","}\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2gluYjccf-IP"},"source":["Now that `ExampleGen` has finished ingesting the data, the next step is data analysis."]},{"cell_type":"markdown","metadata":{"id":"csM6BFhtk5Aa"},"source":["### StatisticsGen\n","The `StatisticsGen` component **computes statistics** over your dataset for data analysis, as well as for use in downstream components. It uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n","\n","`StatisticsGen` takes as input the dataset we just ingested using `ExampleGen`."]},{"cell_type":"code","metadata":{"id":"MAscCCYWgA-9","colab":{"base_uri":"https://localhost:8080/","height":238},"outputId":"94b5e87e-5c5f-4e07-875a-671cc2d72bff"},"source":["statistics_gen = StatisticsGen(\n","    examples=example_gen.outputs['examples'])\n","context.run(statistics_gen)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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 > 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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\">StatisticsGen</span><span class=\"deemphasize\"> at 0x7f97b8815590</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.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","  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\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97bc88c8d0</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 > 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onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: /content/tfx/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f97660af310</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\">/content/tfx/StatisticsGen/statistics/2</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></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\">['stats_options_json']</td><td class = \"attrvalue\">None</td></tr><tr><td class=\"attr-name\">['exclude_splits']</td><td class = \"attrvalue\">[]</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","  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\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97bc88c8d0</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: /content/tfx/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7f9767e35f50</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\">/content/tfx/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\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</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\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97660af2d0</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: /content/tfx/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f97660af310</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\">/content/tfx/StatisticsGen/statistics/2</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></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"],"text/plain":["ExecutionResult(\n","    component_id: StatisticsGen\n","    execution_id: 2\n","    outputs:\n","        statistics: Channel(\n","            type_name: ExampleStatistics\n","            artifacts: [Artifact(artifact: id: 2\n","        type_id: 7\n","        uri: \"/content/tfx/StatisticsGen/statistics/2\"\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: \"statistics\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"producer_component\"\n","          value {\n","            string_value: \"StatisticsGen\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"state\"\n","          value {\n","            string_value: \"published\"\n","          }\n","        }\n","        state: LIVE\n","        , artifact_type: id: 7\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","        ))"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"HLI6cb_5WugZ"},"source":["After `StatisticsGen` finishes running, we can visualize the outputted statistics - **TFDV**. Try playing with the different plots!"]},{"cell_type":"code","metadata":{"id":"tLjXy7K6Tp_G","colab":{"base_uri":"https://localhost:8080/","height":735},"outputId":"84c7c6b1-8bc7-4c86-ae31-45aa9c9c9445"},"source":["context.show(statistics_gen.outputs['statistics'])"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["<b>Artifact at /content/tfx/StatisticsGen/statistics/2</b><br/><br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<div><b>'train' split:</b></div><br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_data_validation/utils/stats_util.py:247: 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":"stdout"},{"output_type":"display_data","data":{"text/html":["<iframe id='facets-iframe' width=\"100%\" height=\"500px\"></iframe>\n","        <script>\n","        facets_iframe = document.getElementById('facets-iframe');\n","        facets_html = '<script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"><\\/script><link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter.html\"><facets-overview 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(A schema defines the expected bounds, types, and properties of the features in your dataset.) It also uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n","\n","Note: The generated schema is best-effort and only tries to infer basic properties of the data. It is expected that you review and modify it as needed.\n","\n","`SchemaGen` will take as input the statistics that we generated with `StatisticsGen`, looking at the training split by default."]},{"cell_type":"code","metadata":{"id":"ygQvZ6hsiQ_J","colab":{"base_uri":"https://localhost:8080/","height":238},"outputId":"4d3388a2-92ee-414d-c625-961d7b83ea86"},"source":["schema_gen = SchemaGen(\n","    statistics=statistics_gen.outputs['statistics'],\n","    infer_feature_shape=False)\n","context.run(schema_gen)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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 > 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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\">SchemaGen</span><span class=\"deemphasize\"> at 0x7f9761984150</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</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 > 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}\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 0x7f97660af2d0</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: 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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\">'Schema'</span> (1 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class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: /content/tfx/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f9761974f90</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\">/content/tfx/SchemaGen/schema/3</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\">['infer_feature_shape']</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">['exclude_splits']</td><td class = \"attrvalue\">[]</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 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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 0x7f97660af2d0</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","  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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\">/content/tfx/StatisticsGen/statistics/2</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></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\">['schema']</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 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.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\">'Schema'</span> (uri: /content/tfx/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f9761974f90</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\">/content/tfx/SchemaGen/schema/3</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"],"text/plain":["ExecutionResult(\n","    component_id: SchemaGen\n","    execution_id: 3\n","    outputs:\n","        schema: Channel(\n","            type_name: Schema\n","            artifacts: [Artifact(artifact: id: 3\n","        type_id: 9\n","        uri: \"/content/tfx/SchemaGen/schema/3\"\n","        custom_properties {\n","          key: \"name\"\n","          value {\n","            string_value: \"schema\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"producer_component\"\n","          value {\n","            string_value: \"SchemaGen\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"state\"\n","          value {\n","            string_value: \"published\"\n","          }\n","        }\n","        state: LIVE\n","        , artifact_type: id: 9\n","        name: \"Schema\"\n","        )]\n","            additional_properties: {}\n","            additional_custom_properties: {}\n","        ))"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"zi6TxTUKXM6b"},"source":["After `SchemaGen` finishes running, we can visualize the generated schema as a table."]},{"cell_type":"code","metadata":{"id":"Ec9vqDXpXeMb","colab":{"base_uri":"https://localhost:8080/","height":355},"outputId":"83c9cdc4-9d39-4ac6-b15c-3f651729c5c9"},"source":["context.show(schema_gen.outputs['schema'])"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["<b>Artifact at /content/tfx/SchemaGen/schema/3</b><br/><br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"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>'dropoff_latitude'</th>\n","      <td>FLOAT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'dropoff_longitude'</th>\n","      <td>FLOAT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'fare'</th>\n","      <td>FLOAT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'pickup_latitude'</th>\n","      <td>FLOAT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'pickup_longitude'</th>\n","      <td>FLOAT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'trip_start_day'</th>\n","      <td>INT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'trip_start_hour'</th>\n","      <td>INT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>'trip_start_month'</th>\n","      <td>INT</td>\n","      <td>required</td>\n","      <td>single</td>\n","      <td>-</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                      Type  Presence Valency Domain\n","Feature name                                       \n","'dropoff_latitude'   FLOAT  required  single      -\n","'dropoff_longitude'  FLOAT  required  single      -\n","'fare'               FLOAT  required  single      -\n","'pickup_latitude'    FLOAT  required  single      -\n","'pickup_longitude'   FLOAT  required  single      -\n","'trip_start_day'       INT  required  single      -\n","'trip_start_hour'      INT  required  single      -\n","'trip_start_month'     INT  required  single      -"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kZWWdbA-m7zp"},"source":["Each feature in your dataset shows up as a row in the schema table, alongside its properties. The schema also captures all the values that a categorical feature takes on, denoted as its domain.\n","\n","To learn more about schemas, see [the SchemaGen documentation](https://www.tensorflow.org/tfx/guide/schemagen)."]},{"cell_type":"markdown","metadata":{"id":"V1qcUuO9k9f8"},"source":["### ExampleValidator\n","The `ExampleValidator` component detects anomalies in your data, based on the expectations defined by the schema. It also uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n","\n","`ExampleValidator` will take as input the statistics from `StatisticsGen`, and the schema from `SchemaGen`."]},{"cell_type":"code","metadata":{"id":"XRlRUuGgiXks","colab":{"base_uri":"https://localhost:8080/","height":363},"outputId":"9f5ab67c-2c41-4cc7-f7e7-275c2fd41391"},"source":["example_validator = ExampleValidator(\n","    statistics=statistics_gen.outputs['statistics'],\n","    schema=schema_gen.outputs['schema'])\n","context.run(example_validator)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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 expanded\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">ExecutionResult</span><span class=\"deemphasize\"> at 0x7f9766186950</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.execution_id</td><td class = \"attrvalue\">4</td></tr><tr><td class=\"attr-name\">.component</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\">ExampleValidator</span><span class=\"deemphasize\"> at 0x7f9766186110</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</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\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97660af2d0</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: /content/tfx/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f97660af310</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\">/content/tfx/StatisticsGen/statistics/2</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></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['schema']</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\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f9761984b50</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",".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\">'Schema'</span> (uri: /content/tfx/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f9761974f90</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\">/content/tfx/SchemaGen/schema/3</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\">['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 0x7f97661867d0</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: /content/tfx/ExampleValidator/anomalies/4)<span class=\"deemphasize\"> at 0x7f97660a1f10</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\">/content/tfx/ExampleValidator/anomalies/4</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></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\">['exclude_splits']</td><td class = \"attrvalue\">[]</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\">['statistics']</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\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97660af2d0</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: /content/tfx/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f97660af310</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\">/content/tfx/StatisticsGen/statistics/2</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></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['schema']</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\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f9761984b50</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",".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\">'Schema'</span> (uri: /content/tfx/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f9761974f90</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\">/content/tfx/SchemaGen/schema/3</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\">['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 0x7f97661867d0</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: /content/tfx/ExampleValidator/anomalies/4)<span class=\"deemphasize\"> at 0x7f97660a1f10</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\">/content/tfx/ExampleValidator/anomalies/4</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></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"],"text/plain":["ExecutionResult(\n","    component_id: ExampleValidator\n","    execution_id: 4\n","    outputs:\n","        anomalies: Channel(\n","            type_name: ExampleAnomalies\n","            artifacts: [Artifact(artifact: id: 4\n","        type_id: 11\n","        uri: \"/content/tfx/ExampleValidator/anomalies/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: \"anomalies\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"producer_component\"\n","          value {\n","            string_value: \"ExampleValidator\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"state\"\n","          value {\n","            string_value: \"published\"\n","          }\n","        }\n","        state: LIVE\n","        , artifact_type: id: 11\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","        ))"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"855mrHgJcoer"},"source":["After `ExampleValidator` finishes running, we can visualize the anomalies as a table."]},{"cell_type":"code","metadata":{"id":"TDyAAozQcrk3","colab":{"base_uri":"https://localhost:8080/","height":254},"outputId":"19fe888e-f999-4981-84ed-5b5d5bf42fb9"},"source":["context.show(example_validator.outputs['anomalies'])"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["<b>Artifact at /content/tfx/ExampleValidator/anomalies/4</b><br/><br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<div><b>'train' split:</b></div><br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow_data_validation/utils/display_util.py:188: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n","  pd.set_option('max_colwidth', -1)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<h4 style=\"color:green;\">No anomalies found.</h4>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<div><b>'eval' split:</b></div><br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<h4 style=\"color:green;\">No anomalies found.</h4>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"znMoJj60ybZx"},"source":["In the anomalies table, we can see that there are no anomalies. This is what we'd expect, since this the first dataset that we've analyzed and the schema is tailored to it. You should review this schema -- anything unexpected means an anomaly in the data. Once reviewed, the schema can be used to guard future data, and anomalies produced here can be used to debug model performance, understand how your data evolves over time, and identify data errors."]},{"cell_type":"code","metadata":{"id":"pwbW2zPKR_S4","colab":{"base_uri":"https://localhost:8080/"},"outputId":"24553f73-1953-4282-dba1-fde70cd58c9c"},"source":["# Get the URI of the output artifact representing the transformed examples, which is a directory\n","train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'train')\n","\n","# Get the list of files in this directory (all compressed TFRecord files)\n","tfrecord_filenames = [os.path.join(train_uri, name)\n","                      for name in os.listdir(train_uri)]\n","\n","# Create a `TFRecordDataset` to read these files\n","dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")\n","\n","# Iterate over the first 1 records and decode them.\n","for tfrecord in dataset.take(1):\n","  serialized_example = tfrecord.numpy()\n","  example = tf.train.Example()\n","  example.ParseFromString(serialized_example)\n","  pp.pprint(example)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["features {\n","  feature {\n","    key: \"dropoff_latitude\"\n","    value {\n","      float_list {\n","        value: 41.92045211791992\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"dropoff_longitude\"\n","    value {\n","      float_list {\n","        value: -87.6799545288086\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"fare\"\n","    value {\n","      float_list {\n","        value: 3.8499999046325684\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_latitude\"\n","    value {\n","      float_list {\n","        value: 41.8996696472168\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"pickup_longitude\"\n","    value {\n","      float_list {\n","        value: -87.66983795166016\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_day\"\n","    value {\n","      int64_list {\n","        value: 6\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_hour\"\n","    value {\n","      int64_list {\n","        value: 15\n","      }\n","    }\n","  }\n","  feature {\n","    key: \"trip_start_month\"\n","    value {\n","      int64_list {\n","        value: 3\n","      }\n","    }\n","  }\n","}\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JtyRz53ZGNDH"},"source":["### Transform\n","We can use TFT here but, specifically I am using other options like constants calculated via Pandas / Numpy etc. These all will be stored on a **constants_trainer.py** file and then used in trainer."]},{"cell_type":"code","metadata":{"id":"GPsP28Zyr3pk"},"source":["bins_lat = pd.qcut(list(df['dropoff_latitude'].values) + list(df['pickup_latitude'].values), q=20, duplicates='drop', retbins=True)[1]\n","bins_lon = pd.qcut(list(df['dropoff_longitude'].values) + list(df['pickup_longitude'].values), q=20, duplicates='drop', retbins=True)[1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"T-zikEmDGkpf"},"source":["code = '''\n","bins_lat = {bins_lat}\n","bins_lon = {bins_lon}\n","'''\n","\n","code = code.replace('{bins_lat}', str(list(bins_lat)))\n","code = code.replace('{bins_lon}', str(list(bins_lon)))\n","\n","with open(_constants_module_file, 'w') as writefile:\n","    writefile.write(code)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"q_b_V6eN4f69"},"source":["After the `Transform` component has transformed your data into features, and the next step is to train a model."]},{"cell_type":"markdown","metadata":{"id":"OBJFtnl6lCg9"},"source":["### Trainer\n","The `Trainer` component will train a model that you define in TensorFlow. Default Trainer support Estimator API, to use Keras API, you need to specify [Generic Trainer](https://github.com/tensorflow/community/blob/master/rfcs/20200117-tfx-generic-trainer.md) by setup `custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor)` in Trainer's contructor.\n","\n","`Trainer` takes as input the schema from `SchemaGen`, the transformed data and graph from `Transform`, training parameters, as well as a module that contains user-defined model code.\n","\n","Will generate two files: \n","- **inputfn_trainer.py** *Data-Feeder to model\n","- **model_trainer.py** *Trainer module"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R0kTyFLBCMNI","outputId":"89740de6-09e8-42a4-ffdc-c360be892853"},"source":["%%writefile {_input_fn_module_file}\n","\n","import os\n","import tensorflow as tf\n","\n","###############################\n","##Feature engineering functions\n","def feature_engg_features(features):\n","  #Add new features\n","  features['distance'] = ((features['pickup_latitude'] - features['dropoff_latitude'])**2 +  (features['pickup_longitude'] - features['dropoff_longitude'])**2)**0.5\n","  features['trip_start_month'] = tf.strings.as_string(features['trip_start_month'])\n","  features['trip_start_hour'] = tf.strings.as_string(features['trip_start_hour'])\n","  features['trip_start_day'] = tf.strings.as_string(features['trip_start_day'])\n","\n","  return(features)\n","\n","#To be called from TF\n","def feature_engg(features, label):\n","  #Add new features\n","  features = feature_engg_features(features)\n","\n","  return(features, label)\n","\n","def make_input_fn(dir_uri, mode, vnum_epochs = None, batch_size = 512):\n","    def decode_tfr(serialized_example):\n","      # 1. define a parser\n","      features = tf.io.parse_example(\n","        serialized_example,\n","        # Defaults are not specified since both keys are required.\n","        features={\n","            'dropoff_latitude': tf.io.FixedLenFeature([], tf.float32),\n","            'dropoff_longitude': tf.io.FixedLenFeature([], tf.float32),\n","            'fare': tf.io.FixedLenFeature([], tf.float32),\n","            'pickup_latitude': tf.io.FixedLenFeature([], tf.float32, default_value = 0.0),\n","            'pickup_longitude': tf.io.FixedLenFeature([], tf.float32, default_value = 0.0),\n","            'trip_start_day': tf.io.FixedLenFeature([], tf.int64),\n","            'trip_start_hour': tf.io.FixedLenFeature([], tf.int64),\n","            'trip_start_month': tf.io.FixedLenFeature([], tf.int64)\n","        })\n","\n","      return features, features['fare']\n","\n","    def _input_fn(v_test=False):\n","      # Get the list of files in this directory (all compressed TFRecord files)\n","      tfrecord_filenames = tf.io.gfile.glob(dir_uri)\n","\n","      # Create a `TFRecordDataset` to read these files\n","      dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")\n","\n","      if mode == tf.estimator.ModeKeys.TRAIN:\n","        num_epochs = vnum_epochs # indefinitely\n","      else:\n","        num_epochs = 1 # end-of-input after this\n","\n","      dataset = dataset.batch(batch_size)\n","      dataset = dataset.prefetch(buffer_size = batch_size)\n","\n","      #Convert TFRecord data to dict\n","      dataset = dataset.map(decode_tfr)\n","\n","      #Feature engineering\n","      dataset = dataset.map(feature_engg)\n","\n","      if mode == tf.estimator.ModeKeys.TRAIN:\n","          num_epochs = vnum_epochs # indefinitely\n","          dataset = dataset.shuffle(buffer_size = batch_size)\n","      else:\n","          num_epochs = 1 # end-of-input after this\n","\n","      dataset = dataset.repeat(num_epochs)       \n","      \n","      #Begins - Uncomment for testing only -----------------------------------------------------<\n","      if v_test == True:\n","        print(next(dataset.__iter__()))\n","        \n","      #End - Uncomment for testing only -----------------------------------------------------<\n","      return dataset\n","    return _input_fn"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Writing inputfn_trainer.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z6QlsOPtDCQE","outputId":"c4ceb299-86c7-45a4-ef88-d55a7872bb85"},"source":["##Test the input function\n","import inputfn_trainer as ift\n","\n","#Test dataset read + Feat Engg function's - output's CSV + Feature engg columns\n","eval_file = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'eval/*')\n","fn_d = ift.make_input_fn(dir_uri = eval_file,\n","                    mode = tf.estimator.ModeKeys.EVAL,\n","                    # vnum_epochs = 1,\n","                    batch_size = 10)\n","\n","fn_d(v_test=True)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["({'dropoff_latitude': <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([41.920452, 41.920452, 41.849247, 42.00157 , 42.00157 , 42.00157 ,\n","       42.00157 , 42.00157 , 41.97244 , 41.89251 ], dtype=float32)>, 'dropoff_longitude': <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([-87.679955, -87.679955, -87.62414 , -87.695015, -87.695015,\n","       -87.695015, -87.695015, -87.695015, -87.67111 , -87.62621 ],\n","      dtype=float32)>, 'fare': <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([12.25,  6.05,  7.85,  7.45,  3.45,  9.85,  9.45,  3.25, 12.25,\n","        6.45], dtype=float32)>, 'pickup_latitude': <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([41.952824, 41.906025, 41.871017, 41.97517 , 42.00157 , 41.968067,\n","       41.97517 , 42.00157 , 41.90838 , 41.89266 ], dtype=float32)>, 'pickup_longitude': <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([-87.653244, -87.67531 , -87.63141 , -87.687515, -87.695015,\n","       -87.72156 , -87.687515, -87.695015, -87.670944, -87.652534],\n","      dtype=float32)>, 'trip_start_day': <tf.Tensor: shape=(10,), dtype=string, numpy=\n","array([b'1', b'5', b'2', b'1', b'5', b'3', b'5', b'4', b'1', b'4'],\n","      dtype=object)>, 'trip_start_hour': <tf.Tensor: shape=(10,), dtype=string, numpy=\n","array([b'1', b'23', b'11', b'12', b'18', b'18', b'22', b'17', b'2', b'22'],\n","      dtype=object)>, 'trip_start_month': <tf.Tensor: shape=(10,), dtype=string, numpy=\n","array([b'10', b'8', b'11', b'3', b'7', b'6', b'3', b'5', b'1', b'4'],\n","      dtype=object)>, 'distance': <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([0.04196864, 0.0151569 , 0.02295252, 0.02744605, 0.        ,\n","       0.04274416, 0.02744605, 0.        , 0.06406043, 0.02632183],\n","      dtype=float32)>}, <tf.Tensor: shape=(10,), dtype=float32, numpy=\n","array([12.25,  6.05,  7.85,  7.45,  3.45,  9.85,  9.45,  3.25, 12.25,\n","        6.45], dtype=float32)>)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<RepeatDataset shapes: ({dropoff_latitude: (None,), dropoff_longitude: (None,), fare: (None,), pickup_latitude: (None,), pickup_longitude: (None,), trip_start_day: (None,), trip_start_hour: (None,), trip_start_month: (None,), distance: (None,)}, (None,)), types: ({dropoff_latitude: tf.float32, dropoff_longitude: tf.float32, fare: tf.float32, pickup_latitude: tf.float32, pickup_longitude: tf.float32, trip_start_day: tf.string, trip_start_hour: tf.string, trip_start_month: tf.string, distance: tf.float32}, tf.float32)>"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"nf9UuNng4YJu","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b921f3b6-6f70-4057-91d6-8e3240756367"},"source":["%%writefile {_model_trainer_module_file}\n","\n","import tensorflow as tf\n","import tensorflow.keras as keras\n","import inputfn_trainer as ift\n","import constants_trainer as ct\n","\n","from tfx.components.trainer.fn_args_utils import FnArgs\n","print(tf.__version__)\n","\n","device = \"gpu\"\n","\n","if device == \"tpu\":\n","  resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n","  tf.config.experimental_connect_to_cluster(resolver)\n","  # This is the TPU initialization code that has to be at the beginning.\n","  tf.tpu.experimental.initialize_tpu_system(resolver)\n","  strategy = tf.distribute.experimental.TPUStrategy(resolver)\n","else:\n","  strategy = tf.distribute.MultiWorkerMirroredStrategy()\n","\n","#Create model\n","params_default = {\n","    'lr' : 0.001,\n","    'beta_1' : 0.99,\n","    'beta_2' : 0.999,\n","    'epsilon' : 1e-08,\n","    'decay' : 0.01,\n","    'hidden_layers' : 1\n","}\n","\n","# Define feature columns(Including feature engineered ones )\n","# These are the features which come from the TF Data pipeline\n","def create_feature_cols():\n","    #Keras format features\n","    k_month = tf.keras.Input(name='trip_start_month', shape=(1,), dtype=tf.string)\n","    k_hour  = tf.keras.Input(name='trip_start_hour', shape=(1,), dtype=tf.string)\n","    k_day  = tf.keras.Input(name='trip_start_day', shape=(1,), dtype=tf.string)\n","    k_picklat  = tf.keras.Input(name='pickup_latitude', shape=(1,), dtype=tf.float32)\n","    k_picklon  = tf.keras.Input(name='pickup_longitude', shape=(1,), dtype=tf.float32)\n","    k_droplat  = tf.keras.Input(name='dropoff_latitude', shape=(1,), dtype=tf.float32)\n","    k_droplon  = tf.keras.Input(name='dropoff_longitude', shape=(1,), dtype=tf.float32)\n","    k_distance  = tf.keras.Input(name='distance', shape=(1,), dtype=tf.float32)\n","    keras_dict_input = {'trip_start_month': k_month, 'trip_start_hour': k_hour, 'trip_start_day' : k_day,\n","                        'pickup_latitude': k_picklat, 'pickup_longitude': k_picklon,\n","                        'dropoff_latitude': k_droplat, 'dropoff_longitude': k_droplon, 'distance' : k_distance\n","                        }\n","\n","    return({'K' : keras_dict_input})\n","\n","def create_keras_model(feature_cols, bins_lat, bins_lon,  params = params_default):\n","    METRICS = [\n","            keras.metrics.RootMeanSquaredError(name='rmse')\n","    ]\n","\n","    #Input layers\n","    input_feats = []\n","    for inp in feature_cols['K'].keys():\n","      input_feats.append(feature_cols['K'][inp])\n","\n","    ##Input processing\n","    ##https://keras.io/examples/structured_data/structured_data_classification_from_scratch/\n","    ##https://github.com/tensorflow/community/blob/master/rfcs/20191212-keras-categorical-inputs.md\n","\n","    ##Handle categorical attributes( One-hot encoding )\n","    cat_day = tf.keras.layers.experimental.preprocessing.StringLookup(vocabulary=['1','2','3','4','5','6','7'], mask_token=None)(feature_cols['K']['trip_start_day'])\n","    cat_day = tf.keras.layers.experimental.preprocessing.CategoryEncoding(max_tokens=7)(cat_day)\n","\n","    cat_hour = tf.keras.layers.experimental.preprocessing.StringLookup(vocabulary=['1','2','3','4','5','6','7','8'\n","                                                                                      '9','10','11','12','13','14','15','16',\n","                                                                                      '17','18','19','20','21','22','23','0'\n","                                                                                      ], mask_token=None)(feature_cols['K']['trip_start_hour'])\n","    cat_hour = tf.keras.layers.experimental.preprocessing.CategoryEncoding(max_tokens=24)(cat_hour)\n","\n","    cat_month = tf.keras.layers.experimental.preprocessing.StringLookup(vocabulary=['1','2','3','4','5','6','7','8'\n","                                                                                      '9','10','11','12'], mask_token=None)(feature_cols['K']['trip_start_month'])\n","    cat_month = tf.keras.layers.experimental.preprocessing.CategoryEncoding(max_tokens=12)(cat_month)\n","\n","    # cat_company = tf.keras.layers.experimental.preprocessing.StringLookup(vocabulary=df['company'].unique(), mask_token=None)(feature_cols['K']['company'])\n","    # cat_company = tf.keras.layers.experimental.preprocessing.CategoryEncoding(max_tokens=len(df['company'].unique()))(cat_company)\n","\n","    ##Binning\n","    bins_pickup_lat = tf.keras.layers.experimental.preprocessing.Discretization(bins = bins_lat)(feature_cols['K']['pickup_latitude'])\n","    cat_pickup_lat = tf.keras.layers.experimental.preprocessing.CategoryEncoding(len(bins_lat)+1)(bins_pickup_lat)\n","\n","    bins_pickup_lon = tf.keras.layers.experimental.preprocessing.Discretization(bins = bins_lon)(feature_cols['K']['pickup_longitude'])\n","    cat_pickup_lon = tf.keras.layers.experimental.preprocessing.CategoryEncoding(len(bins_lon)+1)(bins_pickup_lon)\n","\n","    bins_drop_lat = tf.keras.layers.experimental.preprocessing.Discretization(bins = bins_lat)(feature_cols['K']['dropoff_latitude'])\n","    cat_drop_lat = tf.keras.layers.experimental.preprocessing.CategoryEncoding(len(bins_lat)+1)(bins_drop_lat)\n","\n","    bins_drop_lon = tf.keras.layers.experimental.preprocessing.Discretization(bins = bins_lon)(feature_cols['K']['dropoff_longitude'])\n","    cat_drop_lon = tf.keras.layers.experimental.preprocessing.CategoryEncoding(len(bins_lon)+1)(bins_drop_lon)\n","\n","    ##Categorical cross\n","    cross_day_hour = tf.keras.layers.experimental.preprocessing.CategoryCrossing()([cat_day, cat_hour])\n","    hash_cross_day_hour = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=24 * 7)(cross_day_hour)\n","    cat_cross_day_hour = tf.keras.layers.experimental.preprocessing.CategoryEncoding(max_tokens = 24* 7)(hash_cross_day_hour)\n","\n","    cross_pick_lon_lat = tf.keras.layers.experimental.preprocessing.CategoryCrossing()([cat_pickup_lat, cat_pickup_lon])\n","    hash_cross_pick_lon_lat = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=(len(bins_lat) + 1) ** 2)(cross_pick_lon_lat)\n","\n","    cross_drop_lon_lat = tf.keras.layers.experimental.preprocessing.CategoryCrossing()([cat_drop_lat, cat_drop_lon])\n","    hash_cross_drop_lon_lat = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=(len(bins_lat) + 1) ** 2)(cross_drop_lon_lat)\n","\n","    # Cross to embedding\n","    embed_cross_pick_lon_lat = tf.keras.layers.Embedding(((len(bins_lat) + 1) ** 2), 4)(hash_cross_pick_lon_lat)\n","    embed_cross_pick_lon_lat = tf.reduce_sum(embed_cross_pick_lon_lat, axis=-2)\n","\n","    embed_cross_drop_lon_lat = tf.keras.layers.Embedding(((len(bins_lat) + 1) ** 2), 4)(hash_cross_drop_lon_lat)\n","    embed_cross_drop_lon_lat = tf.reduce_sum(embed_cross_drop_lon_lat, axis=-2)\n","\n","    # Also pass time attributes as Deep signal( Cast to integer )\n","    int_trip_start_day = tf.strings.to_number(feature_cols['K']['trip_start_day'], tf.float32)\n","    int_trip_start_hour = tf.strings.to_number(feature_cols['K']['trip_start_hour'], tf.float32)\n","    int_trip_start_month = tf.strings.to_number(feature_cols['K']['trip_start_month'], tf.float32)\n","\n","    #Add feature engineered columns - LAMBDA layer\n","\n","    ###Create MODEL\n","    ####Concatenate all features( Numerical input )\n","    x_input_numeric = tf.keras.layers.concatenate([\n","                    feature_cols['K']['pickup_latitude'], feature_cols['K']['pickup_longitude'],\n","                    feature_cols['K']['dropoff_latitude'], feature_cols['K']['dropoff_longitude'],\n","                    feature_cols['K']['distance'], embed_cross_pick_lon_lat, embed_cross_drop_lon_lat,\n","                    int_trip_start_day, int_trip_start_hour, int_trip_start_month\n","                    ])\n","\n","    #DEEP - This Dense layer connects to input layer - Numeric Data\n","    x_numeric = tf.keras.layers.Dense(32, activation='relu', kernel_initializer=\"he_uniform\")(x_input_numeric)\n","    x_numeric = tf.keras.layers.BatchNormalization()(x_numeric)\n","\n","    ####Concatenate all Categorical features( Categorical converted )\n","    x_input_categ = tf.keras.layers.concatenate([\n","                    cat_month, cat_cross_day_hour, cat_pickup_lat, cat_pickup_lon,\n","                    cat_drop_lat, cat_drop_lon\n","                    ])\n","    \n","    #WIDE - This Dense layer connects to input layer - Categorical Data\n","    x_categ = tf.keras.layers.Dense(32, activation='relu', kernel_initializer=\"he_uniform\")(x_input_categ)\n","\n","    ####Concatenate both Wide and Deep layers\n","    x = tf.keras.layers.concatenate([x_categ, x_numeric])\n","\n","    for l_ in range(params['hidden_layers']):\n","        x = tf.keras.layers.Dense(32, activation='relu', kernel_initializer=\"he_uniform\",\n","                                  activity_regularizer=tf.keras.regularizers.l2(0.00001))(x)\n","        x = tf.keras.layers.BatchNormalization()(x)\n","\n","    #Final Layer\n","    out = tf.keras.layers.Dense(1, activation='relu')(x)\n","    model = tf.keras.Model(input_feats, out)\n","\n","    #Set optimizer\n","    opt = tf.keras.optimizers.Adam(lr= params['lr'], beta_1=params['beta_1'], \n","                                        beta_2=params['beta_2'], epsilon=params['epsilon'])\n","\n","    #Compile model\n","    model.compile(loss='mean_squared_error',  optimizer=opt, metrics = METRICS)\n","\n","    #Print Summary\n","    print(model.summary())\n","    return model\n","\n","def keras_train_and_evaluate(model, train_dataset, validation_dataset, epochs=100):\n","  #Add callbacks\n","  reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n","                                patience=5, min_lr=0.00001, verbose = 1)\n","  \n","  tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=\"./logs\")\n","\n","  #Train and Evaluate\n","  out = model.fit(train_dataset, \n","                  validation_data = validation_dataset,\n","                  epochs=epochs,\n","                  # validation_steps = 3,   ###Keep this none for running evaluation on full EVAL data every epoch\n","                  steps_per_epoch = 100,   ###Has to be passed - Cant help it :) [ Number of batches per epoch ]\n","                  callbacks=[reduce_lr, #modelsave_callback, #tensorboard_callback, \n","                             keras.callbacks.EarlyStopping(patience=20, restore_best_weights=True, verbose=True)]\n","                  )\n","\n","  return model\n","\n","def save_model(model, model_save_path):\n","  @tf.function\n","  def serving(dropoff_latitude, dropoff_longitude, pickup_latitude, pickup_longitude, trip_start_day, trip_start_hour, trip_start_month):\n","      ##Feature engineering( calculate distance )\n","      distance = tf.cast( tf.sqrt((tf.abs(dropoff_latitude - pickup_latitude))**2 + (tf.abs(dropoff_longitude - pickup_longitude))**2), tf.float32)\n","\n","      payload = {\n","          'dropoff_latitude': dropoff_latitude,\n","          'dropoff_longitude': dropoff_longitude,\n","          'pickup_latitude': pickup_latitude,\n","          'pickup_longitude': pickup_longitude,\n","          'trip_start_day': trip_start_day,\n","          'trip_start_hour': trip_start_hour,\n","          'trip_start_month': trip_start_month,\n","          'distance': distance\n","      }\n","      \n","      ## Predict\n","      ##IF THERE IS AN ERROR IN NUMBER OF PARAMS PASSED HERE OR DATA TYPE THEN IT GIVES ERROR, \"COULDN'T COMPUTE OUTPUT TENSOR\"\n","      predictions = model(payload)\n","      return predictions\n","\n","  serving = serving.get_concrete_function(trip_start_day=tf.TensorSpec([None,], dtype= tf.string, name='trip_start_day'), \n","                                          trip_start_hour=tf.TensorSpec([None,], dtype= tf.string, name='trip_start_hour'),\n","                                          trip_start_month=tf.TensorSpec([None], dtype= tf.string, name='trip_start_month'), \n","                                          dropoff_latitude=tf.TensorSpec([None,], dtype= tf.float32, name='dropoff_latitude'),\n","                                          dropoff_longitude=tf.TensorSpec([None,], dtype= tf.float32, name='dropoff_longitude'), \n","                                          pickup_latitude=tf.TensorSpec([None,], dtype= tf.float32, name='pickup_latitude'),\n","                                          pickup_longitude=tf.TensorSpec([None,], dtype= tf.float32, name='pickup_longitude')\n","                                          )\n","\n","  # version = \"1\"  #{'serving_default': call_output}\n","  tf.saved_model.save(\n","      model,\n","      model_save_path + \"/\",\n","      signatures=serving\n","  )\n","\n","##Main function called by TFX\n","def run_fn(fn_args: FnArgs):\n","  #Create dataset input functions\n","  train_dataset = ift.make_input_fn(dir_uri = fn_args.train_files,\n","                      mode = tf.estimator.ModeKeys.TRAIN,\n","                      batch_size = 128)()\n","\n","  validation_dataset = ift.make_input_fn(dir_uri = fn_args.eval_files,\n","                      mode = tf.estimator.ModeKeys.EVAL,\n","                      batch_size = 512)()\n","\n","  #Create model\n","  m_ = create_keras_model(params = params_default, feature_cols = create_feature_cols(),\n","                          bins_lat = ct.bins_lat,\n","                          bins_lon = ct.bins_lon)\n","  tf.keras.utils.plot_model(m_, show_shapes=True, rankdir=\"LR\")\n","\n","  #Train model\n","  m_ = keras_train_and_evaluate(m_, train_dataset, validation_dataset, fn_args.custom_config['epochs'])\n","\n","  #Save model with custom signature\n","  save_model(m_, fn_args.serving_model_dir)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Writing model_trainer.py\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GY4yTRaX4YJx"},"source":["Now, we pass in this model code to the `Trainer` component and run it to train the model."]},{"cell_type":"code","metadata":{"id":"429-vvCWibO0","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"1297b620-7bc5-4300-adcf-947527f3ce1d"},"source":["trainer = Trainer(\n","    module_file=os.path.abspath(_model_trainer_module_file),\n","    custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),\n","    examples=example_gen.outputs['examples'],\n","    train_args=trainer_pb2.TrainArgs(),\n","    eval_args=trainer_pb2.EvalArgs(),\n","    custom_config=({\"epochs\": 1})\n","    )\n","\n","context.run(trainer)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:absl:From <ipython-input-25-b6a052d874a6>:3: The name tfx.components.base.executor_spec.ExecutorClassSpec is deprecated. Please use tfx.dsl.components.base.executor_spec.ExecutorClassSpec instead.\n"],"name":"stderr"},{"output_type":"stream","text":["2.4.1\n","WARNING:tensorflow:Collective ops is not configured at program startup. Some performance features may not be enabled.\n","INFO:tensorflow:Using MirroredStrategy with devices ('/device:CPU:0',)\n","INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:CPU:0',), communication = CommunicationImplementation.AUTO\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","Model: \"model\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","pickup_latitude (InputLayer)    [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","pickup_longitude (InputLayer)   [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","dropoff_latitude (InputLayer)   [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","dropoff_longitude (InputLayer)  [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","discretization (Discretization) (None, 1)            0           pickup_latitude[0][0]            \n","__________________________________________________________________________________________________\n","discretization_1 (Discretizatio (None, 1)            0           pickup_longitude[0][0]           \n","__________________________________________________________________________________________________\n","discretization_2 (Discretizatio (None, 1)            0           dropoff_latitude[0][0]           \n","__________________________________________________________________________________________________\n","discretization_3 (Discretizatio (None, 1)            0           dropoff_longitude[0][0]          \n","__________________________________________________________________________________________________\n","trip_start_day (InputLayer)     [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","trip_start_hour (InputLayer)    [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","category_encoding_3 (CategoryEn (None, 21)           0           discretization[0][0]             \n","__________________________________________________________________________________________________\n","category_encoding_4 (CategoryEn (None, 21)           0           discretization_1[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_5 (CategoryEn (None, 21)           0           discretization_2[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_6 (CategoryEn (None, 21)           0           discretization_3[0][0]           \n","__________________________________________________________________________________________________\n","string_lookup (StringLookup)    (None, 1)            0           trip_start_day[0][0]             \n","__________________________________________________________________________________________________\n","string_lookup_1 (StringLookup)  (None, 1)            0           trip_start_hour[0][0]            \n","__________________________________________________________________________________________________\n","category_crossing_1 (CategoryCr (None, None)         0           category_encoding_3[0][0]        \n","                                                                 category_encoding_4[0][0]        \n","__________________________________________________________________________________________________\n","category_crossing_2 (CategoryCr (None, None)         0           category_encoding_5[0][0]        \n","                                                                 category_encoding_6[0][0]        \n","__________________________________________________________________________________________________\n","category_encoding (CategoryEnco (None, 7)            0           string_lookup[0][0]              \n","__________________________________________________________________________________________________\n","category_encoding_1 (CategoryEn (None, 24)           0           string_lookup_1[0][0]            \n","__________________________________________________________________________________________________\n","hashing_1 (Hashing)             (None, None)         0           category_crossing_1[0][0]        \n","__________________________________________________________________________________________________\n","hashing_2 (Hashing)             (None, None)         0           category_crossing_2[0][0]        \n","__________________________________________________________________________________________________\n","trip_start_month (InputLayer)   [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","category_crossing (CategoryCros (None, None)         0           category_encoding[0][0]          \n","                                                                 category_encoding_1[0][0]        \n","__________________________________________________________________________________________________\n","embedding (Embedding)           (None, None, 4)      1764        hashing_1[0][0]                  \n","__________________________________________________________________________________________________\n","embedding_1 (Embedding)         (None, None, 4)      1764        hashing_2[0][0]                  \n","__________________________________________________________________________________________________\n","string_lookup_2 (StringLookup)  (None, 1)            0           trip_start_month[0][0]           \n","__________________________________________________________________________________________________\n","hashing (Hashing)               (None, None)         0           category_crossing[0][0]          \n","__________________________________________________________________________________________________\n","distance (InputLayer)           [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","tf.math.reduce_sum (TFOpLambda) (None, 4)            0           embedding[0][0]                  \n","__________________________________________________________________________________________________\n","tf.math.reduce_sum_1 (TFOpLambd (None, 4)            0           embedding_1[0][0]                \n","__________________________________________________________________________________________________\n","tf.strings.to_number (TFOpLambd (None, 1)            0           trip_start_day[0][0]             \n","__________________________________________________________________________________________________\n","tf.strings.to_number_1 (TFOpLam (None, 1)            0           trip_start_hour[0][0]            \n","__________________________________________________________________________________________________\n","tf.strings.to_number_2 (TFOpLam (None, 1)            0           trip_start_month[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_2 (CategoryEn (None, 12)           0           string_lookup_2[0][0]            \n","__________________________________________________________________________________________________\n","category_encoding_7 (CategoryEn (None, 168)          0           hashing[0][0]                    \n","__________________________________________________________________________________________________\n","concatenate (Concatenate)       (None, 16)           0           pickup_latitude[0][0]            \n","                                                                 pickup_longitude[0][0]           \n","                                                                 dropoff_latitude[0][0]           \n","                                                                 dropoff_longitude[0][0]          \n","                                                                 distance[0][0]                   \n","                                                                 tf.math.reduce_sum[0][0]         \n","                                                                 tf.math.reduce_sum_1[0][0]       \n","                                                                 tf.strings.to_number[0][0]       \n","                                                                 tf.strings.to_number_1[0][0]     \n","                                                                 tf.strings.to_number_2[0][0]     \n","__________________________________________________________________________________________________\n","concatenate_1 (Concatenate)     (None, 264)          0           category_encoding_2[0][0]        \n","                                                                 category_encoding_7[0][0]        \n","                                                                 category_encoding_3[0][0]        \n","                                                                 category_encoding_4[0][0]        \n","                                                                 category_encoding_5[0][0]        \n","                                                                 category_encoding_6[0][0]        \n","__________________________________________________________________________________________________\n","dense (Dense)                   (None, 32)           544         concatenate[0][0]                \n","__________________________________________________________________________________________________\n","dense_1 (Dense)                 (None, 32)           8480        concatenate_1[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization (BatchNorma (None, 32)           128         dense[0][0]                      \n","__________________________________________________________________________________________________\n","concatenate_2 (Concatenate)     (None, 64)           0           dense_1[0][0]                    \n","                                                                 batch_normalization[0][0]        \n","__________________________________________________________________________________________________\n","dense_2 (Dense)                 (None, 32)           2080        concatenate_2[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_1 (BatchNor (None, 32)           128         dense_2[0][0]                    \n","__________________________________________________________________________________________________\n","dense_3 (Dense)                 (None, 1)            33          batch_normalization_1[0][0]      \n","==================================================================================================\n","Total params: 14,921\n","Trainable params: 14,793\n","Non-trainable params: 128\n","__________________________________________________________________________________________________\n","None\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:595: UserWarning: Input dict contained keys ['fare'] which did not match any model input. They will be ignored by the model.\n","  [n for n in tensors.keys() if n not in ref_input_names])\n"],"name":"stderr"},{"output_type":"stream","text":["WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","100/100 [==============================] - ETA: 0s - loss: 292.6946 - rmse: 16.9802WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","100/100 [==============================] - 20s 173ms/step - loss: 292.2560 - rmse: 16.9681 - val_loss: 199.9450 - val_rmse: 14.1400\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","INFO:tensorflow:Assets written to: /content/tfx/Trainer/model/5/serving_model_dir/assets\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<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 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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\">Trainer</span><span class=\"deemphasize\"> at 0x7f97662bee10</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.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","  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\">'Examples'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97bc88c8d0</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: /content/tfx/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7f9767e35f50</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\">/content/tfx/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\">.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","}\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","    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.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: /content/tfx/Trainer/model/5)<span class=\"deemphasize\"> at 0x7f9766771050</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\">/content/tfx/Trainer/model/5</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","}\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\">'ModelRun'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97661e9350</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","}\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\">'ModelRun'</span> (uri: /content/tfx/Trainer/model_run/5)<span class=\"deemphasize\"> at 0x7f9766771c10</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\">/content/tfx/Trainer/model_run/5</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\">{}</td></tr><tr><td class=\"attr-name\">['eval_args']</td><td class = \"attrvalue\">{}</td></tr><tr><td class=\"attr-name\">['module_file']</td><td class = \"attrvalue\">/content/model_trainer.py</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\">{&quot;epochs&quot;: 1}</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","  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\">'Examples'</span> (1 artifact)<span 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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: /content/tfx/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7f9767e35f50</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\">/content/tfx/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\">.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","}\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 0x7f97661e93d0</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: /content/tfx/Trainer/model/5)<span class=\"deemphasize\"> at 0x7f9766771050</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\">/content/tfx/Trainer/model/5</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","}\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\">'ModelRun'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f97661e9350</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","}\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\">'ModelRun'</span> (uri: /content/tfx/Trainer/model_run/5)<span class=\"deemphasize\"> at 0x7f9766771c10</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\">/content/tfx/Trainer/model_run/5</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: 5\n","    outputs:\n","        model: Channel(\n","            type_name: Model\n","            artifacts: [Artifact(artifact: id: 5\n","        type_id: 13\n","        uri: \"/content/tfx/Trainer/model/5\"\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","        state: LIVE\n","        , artifact_type: id: 13\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: 6\n","        type_id: 14\n","        uri: \"/content/tfx/Trainer/model_run/5\"\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","        state: LIVE\n","        , artifact_type: id: 14\n","        name: \"ModelRun\"\n","        )]\n","            additional_properties: {}\n","            additional_custom_properties: {}\n","        ))"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"markdown","metadata":{"id":"6Cql1G35StJp"},"source":["#### Analyze Training with TensorBoard\n","Take a peek at the trainer artifact. It points to a directory containing the model subdirectories."]},{"cell_type":"code","metadata":{"id":"bXe62WE0S0Ek"},"source":["model_artifact_dir = trainer.outputs['model'].get()[0].uri\n","pp.pprint(os.listdir(model_artifact_dir))\n","model_dir = os.path.join(model_artifact_dir, 'serving_model_dir')\n","pp.pprint(os.listdir(model_dir))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DfjOmSro6Q3Y"},"source":["Optionally, we can connect TensorBoard to the Trainer to analyze our model's training curves."]},{"cell_type":"code","metadata":{"id":"-APzqz2NeAyj"},"source":["# model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri\n","\n","# %load_ext tensorboard\n","# %tensorboard --logdir {model_run_artifact_dir}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"T8DYekCZlHfj"},"source":["### Pusher\n","The `Pusher` component is usually at the end of a TFX pipeline. It checks whether a model has passed validation, and if so, exports the model to `_serving_model_dir`."]},{"cell_type":"code","metadata":{"id":"r45nQ69eikc9","colab":{"base_uri":"https://localhost:8080/","height":256},"outputId":"6efcf191-16f7-428c-e8af-cdb67a0dbab7"},"source":["pusher = Pusher(\n","    model=trainer.outputs['model'],\n","    push_destination=pusher_pb2.PushDestination(\n","        filesystem=pusher_pb2.PushDestination.Filesystem(\n","            base_directory=_serving_model_dir)))\n","context.run(pusher)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["<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 expanded\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">ExecutionResult</span><span class=\"deemphasize\"> at 0x7f9766662610</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.execution_id</td><td class = \"attrvalue\">6</td></tr><tr><td class=\"attr-name\">.component</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\">Pusher</span><span class=\"deemphasize\"> at 0x7f976666bfd0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['model']</td><td class = 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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 0x7f97661e93d0</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: /content/tfx/Trainer/model/5)<span class=\"deemphasize\"> at 0x7f9766771050</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\">/content/tfx/Trainer/model/5</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 0x7f976666ba50</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: /content/tfx/Pusher/pushed_model/6)<span class=\"deemphasize\"> at 0x7f976629ba50</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\">/content/tfx/Pusher/pushed_model/6</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;/content/tfx/serving_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 0x7f97661e93d0</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: /content/tfx/Trainer/model/5)<span class=\"deemphasize\"> at 0x7f9766771050</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\">/content/tfx/Trainer/model/5</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 0x7f976666ba50</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: /content/tfx/Pusher/pushed_model/6)<span class=\"deemphasize\"> at 0x7f976629ba50</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\">/content/tfx/Pusher/pushed_model/6</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: 6\n","    outputs:\n","        pushed_model: Channel(\n","            type_name: PushedModel\n","            artifacts: [Artifact(artifact: id: 7\n","        type_id: 16\n","        uri: \"/content/tfx/Pusher/pushed_model/6\"\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: \"/content/tfx/serving_model/1616858834\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"pushed_version\"\n","          value {\n","            string_value: \"1616858834\"\n","          }\n","        }\n","        custom_properties {\n","          key: \"state\"\n","          value {\n","            string_value: \"published\"\n","          }\n","        }\n","        state: LIVE\n","        , artifact_type: id: 16\n","        name: \"PushedModel\"\n","        )]\n","            additional_properties: {}\n","            additional_custom_properties: {}\n","        ))"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"ctUErBYoTO9I"},"source":["Let's examine the output artifacts of `Pusher`. "]},{"cell_type":"code","metadata":{"id":"pRkWo-MzTSss","colab":{"base_uri":"https://localhost:8080/"},"outputId":"ed0faafc-46ba-4988-be8d-76cf7d2d51d7"},"source":["pusher.outputs"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'pushed_model': Channel(\n","    type_name: PushedModel\n","    artifacts: [Artifact(artifact: id: 7\n","type_id: 16\n","uri: \"/content/tfx/Pusher/pushed_model/6\"\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: \"/content/tfx/serving_model/1616858834\"\n","  }\n","}\n","custom_properties {\n","  key: \"pushed_version\"\n","  value {\n","    string_value: \"1616858834\"\n","  }\n","}\n","custom_properties {\n","  key: \"state\"\n","  value {\n","    string_value: \"published\"\n","  }\n","}\n","state: LIVE\n",", artifact_type: id: 16\n","name: \"PushedModel\"\n",")]\n","    additional_properties: {}\n","    additional_custom_properties: {}\n",")}"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"markdown","metadata":{"id":"peH2PPS3VgkL"},"source":["In particular, the Pusher will export your model in the SavedModel format, which looks like this:"]},{"cell_type":"code","metadata":{"id":"4zyIqWl9TSdG","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b7ac75f7-04b6-40ba-a519-124f431b0bb9"},"source":["push_uri = pusher.outputs.pushed_model.get()[0].uri\n","model = tf.saved_model.load(push_uri)\n","\n","for item in model.signatures.items():\n","  pp.pprint(item)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7f9766c76320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7f9766b794d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","('serving_default',\n"," <ConcreteFunction signature_wrapper(*, trip_start_hour, dropoff_latitude, dropoff_longitude, pickup_latitude, trip_start_month, trip_start_day, pickup_longitude) at 0x7F975C7EAD90>)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZW7iMG0FkQJj"},"source":["###**Full Pipeline**\n","\n","The pipeline can be run on either of the below Orchestrators:\n","1. Local\n","2. Airflow\n","3. Kubeflow"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GAFjQv7hZ71T","outputId":"0336118f-fd7e-451f-defb-cba0be064389"},"source":["!rm -rf data.*\n","# !rm -rf *trainer.py ##EDIT: Python files have to be retained\n","!rm -rf *.csv\n","!sudo rm -r /content/tfx\n","\n","! cd /content/\n","! mkdir /content/tfx/\n","! mkdir /content/tfx/pipelines\n","! mkdir /content/tfx/metadata\n","! mkdir /content/tfx/logs\n","! mkdir /content/tfx/data\n","! mkdir /content/tfx/serving_model\n","\n","! mkdir /content/train_data/\n","! mkdir /content/eval_data/\n","\n","!wget https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-03-27 15:29:18--  https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1922812 (1.8M) [text/plain]\n","Saving to: ‘data.csv’\n","\n","data.csv            100%[===================>]   1.83M  --.-KB/s    in 0.08s   \n","\n","2021-03-27 15:29:18 (22.8 MB/s) - ‘data.csv’ saved [1922812/1922812]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RtsNVH2QaWRA","outputId":"5d01b848-b21b-45b1-9caf-2b38d7058f02"},"source":["df = pd.read_csv('/content/data.csv')\n","\n","##Drop useless columns\n","df = df.drop(['trip_start_timestamp','trip_miles','pickup_census_tract',\n","              'dropoff_census_tract','trip_seconds','payment_type','tips', \n","              'company','dropoff_community_area','pickup_community_area'], axis=1)\n","\n","#Drop NA rows\n","df = df.dropna()\n","\n","##Keep a test set for final testing( TFX internally splits train and validation data )\n","np.random.seed(seed=2)\n","msk = np.random.rand(len(df)) < 0.9\n","traindf = df[msk]\n","evaldf = df[~msk]\n","\n","print(len(traindf))\n","print(len(evaldf))\n","\n","traindf.to_csv(\"/content/train_data/data.csv\", index=False, header=True)\n","evaldf.to_csv(\"/content/eval_data/eval.csv\", index=False, header=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["13077\n","1442\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6RLIo8_LeB9k"},"source":["# https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/\n","def create_final_pipeline(\n","    pipeline_name: Text,\n","    root_path: Text,\n","    data_path: Text,\n","    training_params: Dict[Text, Text],\n","    # beam_pipeline_args: List[Text],\n",") -> pipeline.Pipeline:\n","\n","  _pipeline_root = os.path.join(root_path, 'pipelines');      # Join ~/tfx/pipelines/\n","  _metadata_db_root = os.path.join(root_path, 'metadata.db');    # Join ~/tfx/metadata.db\n","  _log_root = os.path.join(root_path, 'logs');\n","  _model_root = os.path.join(root_path, 'model');\n","  _serving_model_dir = os.path.join(root_path, 'serving_model')\n","\n","  # Full pipeline\n","  example_gen = CsvExampleGen(input=external_input(data_path))\n","\n","  statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])\n","\n","  infer_schema = SchemaGen(\n","      statistics=statistics_gen.outputs['statistics'], infer_feature_shape=False)\n","\n","  validate_stats = ExampleValidator(\n","    statistics=statistics_gen.outputs['statistics'],\n","    schema=infer_schema.outputs['schema'])\n","\n","  trainer = Trainer(\n","      module_file=os.path.abspath(_model_trainer_module_file),\n","      custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),\n","      examples=example_gen.outputs['examples'],\n","      train_args=trainer_pb2.TrainArgs(),\n","      eval_args=trainer_pb2.EvalArgs(),\n","      custom_config=(training_params)\n","      )\n","\n","  pusher = Pusher(\n","      model=trainer.outputs['model'],\n","      push_destination=pusher_pb2.PushDestination(\n","          filesystem=pusher_pb2.PushDestination.Filesystem(\n","              base_directory=_serving_model_dir)))\n","\n","  # This pipeline obj carries the business logic of the pipeline, but no runner-specific information\n","  # was included.\n","  return pipeline.Pipeline(\n","    pipeline_name=  pipeline_name,\n","    pipeline_root=  root_path,\n","    components=[\n","        example_gen, statistics_gen, infer_schema, validate_stats,\n","        trainer, pusher\n","    ],\n","    # metadata_connection_config = metadata.sqlite_metadata_connection_config(_metadata_db_root),\n","    metadata_connection_config = metadata.sqlite_metadata_connection_config(_metadata_db_root),\n","    enable_cache=True,\n","    beam_pipeline_args=['--direct_num_workers=%d' % 0],\n","  )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7cb_oBqYfjHr","outputId":"7d47d1a8-a092-40e7-fb86-24b78a009a4b"},"source":["#Run pipeline locally\n","from tfx.orchestration.local.local_dag_runner import LocalDagRunner\n","\n","##Define all paths\n","_tfx_root = os.path.join(os.getcwd(), 'tfx')\n","\n","#Config params\n","training_params = {\"epochs\": 50}\n","\n","#Create and run pipeline\n","p_ = create_final_pipeline(root_path = _tfx_root, \n","                           pipeline_name=\"local_pipeline\", \n","                           data_path=\"/content/train_data\",\n","                           training_params=training_params)\n","\n","LocalDagRunner().run(p_)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:absl:The \"input\" argument to the CsvExampleGen component has been deprecated by \"input_base\". Please update your usage as support for this argument will be removed soon.\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n","WARNING:absl:If direct_num_workers is not equal to 1, direct_running_mode should be `multi_processing` or `multi_threading` instead of `in_memory` in order for it to have the desired worker parallelism effect.\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n","WARNING:absl:If direct_num_workers is not equal to 1, direct_running_mode should be `multi_processing` or `multi_threading` instead of `in_memory` in order for it to have the desired worker parallelism effect.\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n"],"name":"stderr"},{"output_type":"stream","text":["WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","Model: \"model_1\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","pickup_latitude (InputLayer)    [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","pickup_longitude (InputLayer)   [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","dropoff_latitude (InputLayer)   [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","dropoff_longitude (InputLayer)  [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","discretization_4 (Discretizatio (None, 1)            0           pickup_latitude[0][0]            \n","__________________________________________________________________________________________________\n","discretization_5 (Discretizatio (None, 1)            0           pickup_longitude[0][0]           \n","__________________________________________________________________________________________________\n","discretization_6 (Discretizatio (None, 1)            0           dropoff_latitude[0][0]           \n","__________________________________________________________________________________________________\n","discretization_7 (Discretizatio (None, 1)            0           dropoff_longitude[0][0]          \n","__________________________________________________________________________________________________\n","trip_start_day (InputLayer)     [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","trip_start_hour (InputLayer)    [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","category_encoding_11 (CategoryE (None, 21)           0           discretization_4[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_12 (CategoryE (None, 21)           0           discretization_5[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_13 (CategoryE (None, 21)           0           discretization_6[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_14 (CategoryE (None, 21)           0           discretization_7[0][0]           \n","__________________________________________________________________________________________________\n","string_lookup_3 (StringLookup)  (None, 1)            0           trip_start_day[0][0]             \n","__________________________________________________________________________________________________\n","string_lookup_4 (StringLookup)  (None, 1)            0           trip_start_hour[0][0]            \n","__________________________________________________________________________________________________\n","category_crossing_4 (CategoryCr (None, None)         0           category_encoding_11[0][0]       \n","                                                                 category_encoding_12[0][0]       \n","__________________________________________________________________________________________________\n","category_crossing_5 (CategoryCr (None, None)         0           category_encoding_13[0][0]       \n","                                                                 category_encoding_14[0][0]       \n","__________________________________________________________________________________________________\n","category_encoding_8 (CategoryEn (None, 7)            0           string_lookup_3[0][0]            \n","__________________________________________________________________________________________________\n","category_encoding_9 (CategoryEn (None, 24)           0           string_lookup_4[0][0]            \n","__________________________________________________________________________________________________\n","hashing_4 (Hashing)             (None, None)         0           category_crossing_4[0][0]        \n","__________________________________________________________________________________________________\n","hashing_5 (Hashing)             (None, None)         0           category_crossing_5[0][0]        \n","__________________________________________________________________________________________________\n","trip_start_month (InputLayer)   [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","category_crossing_3 (CategoryCr (None, None)         0           category_encoding_8[0][0]        \n","                                                                 category_encoding_9[0][0]        \n","__________________________________________________________________________________________________\n","embedding_2 (Embedding)         (None, None, 4)      1764        hashing_4[0][0]                  \n","__________________________________________________________________________________________________\n","embedding_3 (Embedding)         (None, None, 4)      1764        hashing_5[0][0]                  \n","__________________________________________________________________________________________________\n","string_lookup_5 (StringLookup)  (None, 1)            0           trip_start_month[0][0]           \n","__________________________________________________________________________________________________\n","hashing_3 (Hashing)             (None, None)         0           category_crossing_3[0][0]        \n","__________________________________________________________________________________________________\n","distance (InputLayer)           [(None, 1)]          0                                            \n","__________________________________________________________________________________________________\n","tf.math.reduce_sum_2 (TFOpLambd (None, 4)            0           embedding_2[0][0]                \n","__________________________________________________________________________________________________\n","tf.math.reduce_sum_3 (TFOpLambd (None, 4)            0           embedding_3[0][0]                \n","__________________________________________________________________________________________________\n","tf.strings.to_number_3 (TFOpLam (None, 1)            0           trip_start_day[0][0]             \n","__________________________________________________________________________________________________\n","tf.strings.to_number_4 (TFOpLam (None, 1)            0           trip_start_hour[0][0]            \n","__________________________________________________________________________________________________\n","tf.strings.to_number_5 (TFOpLam (None, 1)            0           trip_start_month[0][0]           \n","__________________________________________________________________________________________________\n","category_encoding_10 (CategoryE (None, 12)           0           string_lookup_5[0][0]            \n","__________________________________________________________________________________________________\n","category_encoding_15 (CategoryE (None, 168)          0           hashing_3[0][0]                  \n","__________________________________________________________________________________________________\n","concatenate_3 (Concatenate)     (None, 16)           0           pickup_latitude[0][0]            \n","                                                                 pickup_longitude[0][0]           \n","                                                                 dropoff_latitude[0][0]           \n","                                                                 dropoff_longitude[0][0]          \n","                                                                 distance[0][0]                   \n","                                                                 tf.math.reduce_sum_2[0][0]       \n","                                                                 tf.math.reduce_sum_3[0][0]       \n","                                                                 tf.strings.to_number_3[0][0]     \n","                                                                 tf.strings.to_number_4[0][0]     \n","                                                                 tf.strings.to_number_5[0][0]     \n","__________________________________________________________________________________________________\n","concatenate_4 (Concatenate)     (None, 264)          0           category_encoding_10[0][0]       \n","                                                                 category_encoding_15[0][0]       \n","                                                                 category_encoding_11[0][0]       \n","                                                                 category_encoding_12[0][0]       \n","                                                                 category_encoding_13[0][0]       \n","                                                                 category_encoding_14[0][0]       \n","__________________________________________________________________________________________________\n","dense_4 (Dense)                 (None, 32)           544         concatenate_3[0][0]              \n","__________________________________________________________________________________________________\n","dense_5 (Dense)                 (None, 32)           8480        concatenate_4[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_2 (BatchNor (None, 32)           128         dense_4[0][0]                    \n","__________________________________________________________________________________________________\n","concatenate_5 (Concatenate)     (None, 64)           0           dense_5[0][0]                    \n","                                                                 batch_normalization_2[0][0]      \n","__________________________________________________________________________________________________\n","dense_6 (Dense)                 (None, 32)           2080        concatenate_5[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_3 (BatchNor (None, 32)           128         dense_6[0][0]                    \n","__________________________________________________________________________________________________\n","dense_7 (Dense)                 (None, 1)            33          batch_normalization_3[0][0]      \n","==================================================================================================\n","Total params: 14,921\n","Trainable params: 14,793\n","Non-trainable params: 128\n","__________________________________________________________________________________________________\n","None\n","Epoch 1/50\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:595: UserWarning: Input dict contained keys ['fare'] which did not match any model input. They will be ignored by the model.\n","  [n for n in tensors.keys() if n not in ref_input_names])\n"],"name":"stderr"},{"output_type":"stream","text":["WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","100/100 [==============================] - ETA: 0s - loss: 217.0832 - rmse: 14.7035WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","100/100 [==============================] - 19s 169ms/step - loss: 217.1244 - rmse: 14.7052 - val_loss: 214.2426 - val_rmse: 14.6367\n","Epoch 2/50\n","100/100 [==============================] - 16s 162ms/step - loss: 194.8064 - rmse: 13.8886 - val_loss: 298.7778 - val_rmse: 17.2851\n","Epoch 3/50\n","100/100 [==============================] - 16s 160ms/step - loss: 182.8486 - rmse: 13.4537 - val_loss: 475.0226 - val_rmse: 21.7950\n","Epoch 4/50\n","100/100 [==============================] - 16s 160ms/step - loss: 120.3823 - rmse: 10.8827 - val_loss: 322.9235 - val_rmse: 17.9700\n","Epoch 5/50\n","100/100 [==============================] - 16s 162ms/step - loss: 81.6593 - rmse: 8.9703 - val_loss: 70.8273 - val_rmse: 8.4159\n","Epoch 6/50\n","100/100 [==============================] - 16s 160ms/step - loss: 57.8599 - rmse: 7.5910 - val_loss: 40.0003 - val_rmse: 6.3246\n","Epoch 7/50\n","100/100 [==============================] - 16s 160ms/step - loss: 164.7924 - rmse: 12.6626 - val_loss: 59.6570 - val_rmse: 7.7238\n","Epoch 8/50\n","100/100 [==============================] - 16s 162ms/step - loss: 194.9424 - rmse: 12.8607 - val_loss: 56.2903 - val_rmse: 7.5027\n","Epoch 9/50\n","100/100 [==============================] - 16s 160ms/step - loss: 33.9889 - rmse: 5.7552 - val_loss: 36.6010 - val_rmse: 6.0499\n","Epoch 10/50\n","100/100 [==============================] - 16s 161ms/step - loss: 63.0520 - rmse: 7.7663 - val_loss: 40.7972 - val_rmse: 6.3873\n","Epoch 11/50\n","100/100 [==============================] - 16s 160ms/step - loss: 41.0774 - rmse: 6.2706 - val_loss: 59.2725 - val_rmse: 7.6989\n","Epoch 12/50\n","100/100 [==============================] - 16s 161ms/step - loss: 75.1524 - rmse: 8.3895 - val_loss: 102.4453 - val_rmse: 10.1215\n","Epoch 13/50\n","100/100 [==============================] - 16s 160ms/step - loss: 89.3488 - rmse: 9.1501 - val_loss: 105.3505 - val_rmse: 10.2640\n","Epoch 14/50\n","100/100 [==============================] - 16s 161ms/step - loss: 51.3950 - rmse: 6.9459 - val_loss: 198.9368 - val_rmse: 14.1045\n","\n","Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n","Epoch 15/50\n","100/100 [==============================] - 16s 160ms/step - loss: 62.9374 - rmse: 7.6894 - val_loss: 104.5761 - val_rmse: 10.2262\n","Epoch 16/50\n","100/100 [==============================] - 16s 160ms/step - loss: 196.7807 - rmse: 12.6815 - val_loss: 57.5709 - val_rmse: 7.5875\n","Epoch 17/50\n","100/100 [==============================] - 16s 162ms/step - loss: 55.5935 - rmse: 7.2975 - val_loss: 44.2611 - val_rmse: 6.6529\n","Epoch 18/50\n","100/100 [==============================] - 16s 159ms/step - loss: 30.2384 - rmse: 5.4320 - val_loss: 34.3575 - val_rmse: 5.8615\n","Epoch 19/50\n","100/100 [==============================] - 16s 161ms/step - loss: 88.3100 - rmse: 9.0977 - val_loss: 33.1711 - val_rmse: 5.7594\n","Epoch 20/50\n","100/100 [==============================] - 16s 159ms/step - loss: 144.0971 - rmse: 10.9913 - val_loss: 30.8044 - val_rmse: 5.5502\n","Epoch 21/50\n","100/100 [==============================] - 16s 161ms/step - loss: 49.9575 - rmse: 6.7986 - val_loss: 28.9896 - val_rmse: 5.3842\n","Epoch 22/50\n","100/100 [==============================] - 16s 161ms/step - loss: 69.0783 - rmse: 7.9757 - val_loss: 29.1548 - val_rmse: 5.3995\n","Epoch 23/50\n","100/100 [==============================] - 16s 161ms/step - loss: 36.7930 - rmse: 5.6760 - val_loss: 29.8607 - val_rmse: 5.4645\n","Epoch 24/50\n","100/100 [==============================] - 16s 160ms/step - loss: 34.0130 - rmse: 5.7177 - val_loss: 28.2520 - val_rmse: 5.3153\n","Epoch 25/50\n","100/100 [==============================] - 16s 162ms/step - loss: 55.6878 - rmse: 7.2212 - val_loss: 29.9096 - val_rmse: 5.4690\n","Epoch 26/50\n","100/100 [==============================] - 16s 160ms/step - loss: 52.0275 - rmse: 6.9239 - val_loss: 28.9011 - val_rmse: 5.3760\n","Epoch 27/50\n","100/100 [==============================] - 16s 161ms/step - loss: 29.6021 - rmse: 5.3416 - val_loss: 27.8313 - val_rmse: 5.2755\n","Epoch 28/50\n","100/100 [==============================] - 16s 160ms/step - loss: 72.0424 - rmse: 8.1916 - val_loss: 30.4622 - val_rmse: 5.5192\n","Epoch 29/50\n","100/100 [==============================] - 16s 161ms/step - loss: 86.4868 - rmse: 9.0198 - val_loss: 28.6383 - val_rmse: 5.3515\n","Epoch 30/50\n","100/100 [==============================] - 16s 161ms/step - loss: 47.8823 - rmse: 6.6413 - val_loss: 28.2398 - val_rmse: 5.3141\n","Epoch 31/50\n","100/100 [==============================] - 16s 160ms/step - loss: 39.5788 - rmse: 6.1364 - val_loss: 31.4934 - val_rmse: 5.6119\n","Epoch 32/50\n","100/100 [==============================] - 16s 161ms/step - loss: 71.5713 - rmse: 8.1904 - val_loss: 31.4563 - val_rmse: 5.6086\n","\n","Epoch 00032: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n","Epoch 33/50\n","100/100 [==============================] - 16s 160ms/step - loss: 68.4022 - rmse: 7.9015 - val_loss: 27.0354 - val_rmse: 5.1996\n","Epoch 34/50\n","100/100 [==============================] - 16s 162ms/step - loss: 53.8253 - rmse: 7.0993 - val_loss: 27.0893 - val_rmse: 5.2047\n","Epoch 35/50\n","100/100 [==============================] - 16s 161ms/step - loss: 65.6114 - rmse: 7.7330 - val_loss: 27.2562 - val_rmse: 5.2207\n","Epoch 36/50\n","100/100 [==============================] - 16s 161ms/step - loss: 49.9068 - rmse: 6.8633 - val_loss: 26.9264 - val_rmse: 5.1891\n","Epoch 37/50\n","100/100 [==============================] - 16s 164ms/step - loss: 22.5058 - rmse: 4.7386 - val_loss: 26.7290 - val_rmse: 5.1700\n","Epoch 38/50\n","100/100 [==============================] - 16s 161ms/step - loss: 104.6029 - rmse: 9.8482 - val_loss: 27.9150 - val_rmse: 5.2835\n","Epoch 39/50\n","100/100 [==============================] - 16s 163ms/step - loss: 137.3150 - rmse: 10.6986 - val_loss: 28.0951 - val_rmse: 5.3005\n","Epoch 40/50\n","100/100 [==============================] - 16s 161ms/step - loss: 28.5468 - rmse: 5.2845 - val_loss: 26.5959 - val_rmse: 5.1571\n","Epoch 41/50\n","100/100 [==============================] - 16s 162ms/step - loss: 125.1656 - rmse: 10.4386 - val_loss: 27.3829 - val_rmse: 5.2329\n","Epoch 42/50\n","100/100 [==============================] - 16s 161ms/step - loss: 43.6788 - rmse: 6.3620 - val_loss: 27.7053 - val_rmse: 5.2636\n","Epoch 43/50\n","100/100 [==============================] - 16s 163ms/step - loss: 53.9686 - rmse: 7.0948 - val_loss: 26.9982 - val_rmse: 5.1960\n","Epoch 44/50\n","100/100 [==============================] - 16s 162ms/step - loss: 68.4513 - rmse: 7.9779 - val_loss: 26.8975 - val_rmse: 5.1863\n","Epoch 45/50\n","100/100 [==============================] - 16s 163ms/step - loss: 29.4764 - rmse: 5.2811 - val_loss: 27.5113 - val_rmse: 5.2451\n","\n","Epoch 00045: ReduceLROnPlateau reducing learning rate to 1e-05.\n","Epoch 46/50\n","100/100 [==============================] - 16s 163ms/step - loss: 63.5452 - rmse: 7.7999 - val_loss: 28.2573 - val_rmse: 5.3157\n","Epoch 47/50\n","100/100 [==============================] - 16s 161ms/step - loss: 37.5531 - rmse: 5.9227 - val_loss: 26.8318 - val_rmse: 5.1799\n","Epoch 48/50\n","100/100 [==============================] - 16s 162ms/step - loss: 35.7494 - rmse: 5.8155 - val_loss: 26.7775 - val_rmse: 5.1747\n","Epoch 49/50\n","100/100 [==============================] - 16s 162ms/step - loss: 63.8763 - rmse: 7.6909 - val_loss: 27.1194 - val_rmse: 5.2076\n","Epoch 50/50\n","100/100 [==============================] - 16s 163ms/step - loss: 54.1033 - rmse: 7.1340 - val_loss: 25.7967 - val_rmse: 5.0790\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","WARNING:tensorflow:Using a while_loop for converting BoostedTreesBucketize\n","INFO:tensorflow:Assets written to: /content/tfx/Trainer/model/3/serving_model_dir/assets\n"],"name":"stdout"},{"output_type":"stream","text":["WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n","WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n","WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['-f', '/root/.local/share/jupyter/runtime/kernel-7fb6cf45-63a7-4aa3-9d25-51f93b63ad96.json']\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"YjkNK4tjThRD"},"source":["### **Inference**( saved_model_cli )"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x1dvXxsiSSSJ","outputId":"7b2925d8-9fef-4feb-dbc4-46e13d8f7c77"},"source":["!saved_model_cli show --dir \"/content/tfx/Pusher/pushed_model/4\" --all"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\n","MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n","\n","signature_def['__saved_model_init_op']:\n","  The given SavedModel SignatureDef contains the following input(s):\n","  The given SavedModel SignatureDef contains the following output(s):\n","    outputs['__saved_model_init_op'] tensor_info:\n","        dtype: DT_INVALID\n","        shape: unknown_rank\n","        name: NoOp\n","  Method name is: \n","\n","signature_def['serving_default']:\n","  The given SavedModel SignatureDef contains the following input(s):\n","    inputs['dropoff_latitude'] tensor_info:\n","        dtype: DT_FLOAT\n","        shape: (-1)\n","        name: serving_default_dropoff_latitude:0\n","    inputs['dropoff_longitude'] tensor_info:\n","        dtype: DT_FLOAT\n","        shape: (-1)\n","        name: serving_default_dropoff_longitude:0\n","    inputs['pickup_latitude'] tensor_info:\n","        dtype: DT_FLOAT\n","        shape: (-1)\n","        name: serving_default_pickup_latitude:0\n","    inputs['pickup_longitude'] tensor_info:\n","        dtype: DT_FLOAT\n","        shape: (-1)\n","        name: serving_default_pickup_longitude:0\n","    inputs['trip_start_day'] tensor_info:\n","        dtype: DT_STRING\n","        shape: (-1)\n","        name: serving_default_trip_start_day:0\n","    inputs['trip_start_hour'] tensor_info:\n","        dtype: DT_STRING\n","        shape: (-1)\n","        name: serving_default_trip_start_hour:0\n","    inputs['trip_start_month'] tensor_info:\n","        dtype: DT_STRING\n","        shape: (-1)\n","        name: serving_default_trip_start_month:0\n","  The given SavedModel SignatureDef contains the following output(s):\n","    outputs['output_0'] tensor_info:\n","        dtype: DT_FLOAT\n","        shape: (-1, 1)\n","        name: StatefulPartitionedCall:0\n","  Method name is: tensorflow/serving/predict\n","Traceback (most recent call last):\n","  File \"/usr/local/bin/saved_model_cli\", line 8, in <module>\n","    sys.exit(main())\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/tools/saved_model_cli.py\", line 990, in main\n","    args.func(args)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/tools/saved_model_cli.py\", line 691, in show\n","    _show_all(args.dir)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/tools/saved_model_cli.py\", line 283, in _show_all\n","    _show_defined_functions(saved_model_dir)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/tools/saved_model_cli.py\", line 176, in _show_defined_functions\n","    trackable_object = load.load(saved_model_dir)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/saved_model/load.py\", line 528, in load\n","    return load_internal(export_dir, tags)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/saved_model/load.py\", line 552, in load_internal\n","    export_dir)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/saved_model/load.py\", line 114, in __init__\n","    meta_graph.graph_def.library))\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/saved_model/function_deserialization.py\", line 312, in load_function_def_library\n","    func_graph = function_def_lib.function_def_to_graph(copy)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/framework/function_def_to_graph.py\", line 59, in function_def_to_graph\n","    fdef, input_shapes)\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/framework/function_def_to_graph.py\", line 218, in function_def_to_graph_def\n","    op_def = default_graph._get_op_def(node_def.op)  # pylint: disable=protected-access\n","  File \"/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/framework/ops.py\", line 3712, in _get_op_def\n","    c_api.TF_GraphGetOpDef(self._c_graph, compat.as_bytes(type), buf)\n","tensorflow.python.framework.errors_impl.NotFoundError: Op type not registered 'DenseBincount' in binary running on 9fac1a128a27. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ws9TkuU5SpdU","outputId":"4d8d60aa-b2b0-4cdb-88bd-26e2fb1a8dbf"},"source":["#LOCAL: Predict using Keras prediction function\n","saved_mod = tf.saved_model.load(\"/content/tfx/Pusher/pushed_model/4\")\n","\n","#Get prediction function from serving\n","f = saved_mod.signatures['serving_default']\n","\n","#Run prediction function from serving\n","f(dropoff_latitude=tf.convert_to_tensor([41.920452]), dropoff_longitude = tf.convert_to_tensor([-87.679955]), pickup_latitude = tf.convert_to_tensor([41.952823]), \n","  pickup_longitude =tf.convert_to_tensor([-87.653244]), trip_start_day=tf.convert_to_tensor([\"1\"]), trip_start_hour=tf.convert_to_tensor([\"5\"]),\n","  trip_start_month=tf.convert_to_tensor([\"6\"]))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f9760982f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f975d33f320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f975f8ca050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f975d3b73b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f9760973d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f97609ceb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f975bb2eb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f975bb3cef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f975bb4f290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["{'output_0': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[11.291443]], dtype=float32)>}"]},"metadata":{"tags":[]},"execution_count":36}]}]}