TFX Estimator Component Tutorial

A Component-by-Component Introduction to TensorFlow Extended (TFX)

This Colab-based tutorial will interactively walk through each built-in component of TensorFlow Extended (TFX).

It covers every step in an end-to-end machine learning pipeline, from data ingestion to pushing a model to serving.

When you're done, the contents of this notebook can be automatically exported as TFX pipeline source code, which you can orchestrate with Apache Airflow and Apache Beam.

Background

This notebook demonstrates how to use TFX in a Jupyter/Colab environment. Here, we walk through the Chicago Taxi example in an interactive notebook.

Working in an interactive notebook is a useful way to become familiar with the structure of a TFX pipeline. It's also useful when doing development of your own pipelines as a lightweight development environment, but you should be aware that there are differences in the way interactive notebooks are orchestrated, and how they access metadata artifacts.

Orchestration

In a production deployment of TFX, you will use an orchestrator such as Apache Airflow, Kubeflow Pipelines, or Apache Beam to orchestrate a pre-defined pipeline graph of TFX components. In an interactive notebook, the notebook itself is the orchestrator, running each TFX component as you execute the notebook cells.

Metadata

In a production deployment of TFX, you will access metadata through the ML Metadata (MLMD) API. MLMD stores metadata properties in a database such as MySQL or SQLite, and stores the metadata payloads in a persistent store such as on your filesystem. In an interactive notebook, both properties and payloads are stored in an ephemeral SQLite database in the /tmp directory on the Jupyter notebook or Colab server.

Setup

First, we install and import the necessary packages, set up paths, and download data.

Upgrade Pip

To avoid upgrading Pip in a system when running locally, check to make sure that we're running in Colab. Local systems can of course be upgraded separately.

try:
  import colab
  !pip install --upgrade pip
except:
  pass

Install TFX

pip install -q -U tfx

Did you restart the runtime?

If you are using Google Colab, the first time that you run the cell above, you must restart the runtime (Runtime > Restart runtime ...). This is because of the way that Colab loads packages.

Import packages

We import necessary packages, including standard TFX component classes.

import os
import pprint
import tempfile
import urllib

import absl
import tensorflow as tf
import tensorflow_model_analysis as tfma
tf.get_logger().propagate = False
pp = pprint.PrettyPrinter()

import tfx
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import ResolverNode
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.dsl.experimental import latest_blessed_model_resolver
from tfx.orchestration import metadata
from tfx.orchestration import pipeline
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.proto import pusher_pb2
from tfx.proto import trainer_pb2
from tfx.proto.evaluator_pb2 import SingleSlicingSpec
from tfx.utils.dsl_utils import external_input
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing

%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip

WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.

Let's check the library versions.

print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))

TensorFlow version: 2.4.1
TFX version: 0.29.0

Set up pipeline paths

# This is the root directory for your TFX pip package installation.
_tfx_root = tfx.__path__[0]

# This is the directory containing the TFX Chicago Taxi Pipeline example.
_taxi_root = os.path.join(_tfx_root, 'examples/chicago_taxi_pipeline')

# This is the path where your model will be pushed for serving.
_serving_model_dir = os.path.join(
    tempfile.mkdtemp(), 'serving_model/taxi_simple')

# Set up logging.
absl.logging.set_verbosity(absl.logging.INFO)

Download example data

We download the example dataset for use in our TFX pipeline.

The dataset we're using is the Taxi Trips dataset released by the City of Chicago. The columns in this dataset are:

pickup_community_area	fare	trip_start_month
trip_start_hour	trip_start_day	trip_start_timestamp
pickup_latitude	pickup_longitude	dropoff_latitude
dropoff_longitude	trip_miles	pickup_census_tract
dropoff_census_tract	payment_type	company
trip_seconds	dropoff_community_area	tips

With this dataset, we will build a model that predicts the tips of a trip.

_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)

('/tmp/tfx-dataif8ib649/data.csv', <http.client.HTTPMessage at 0x7f259b525470>)

Take a quick look at the CSV file.

head {_data_filepath}

pickup_community_area,fare,trip_start_month,trip_start_hour,trip_start_day,trip_start_timestamp,pickup_latitude,pickup_longitude,dropoff_latitude,dropoff_longitude,trip_miles,pickup_census_tract,dropoff_census_tract,payment_type,company,trip_seconds,dropoff_community_area,tips
,12.45,5,19,6,1400269500,,,,,0.0,,,Credit Card,Chicago Elite Cab Corp. (Chicago Carriag,0,,0.0
,0,3,19,5,1362683700,,,,,0,,,Unknown,Chicago Elite Cab Corp.,300,,0
60,27.05,10,2,3,1380593700,41.836150155,-87.648787952,,,12.6,,,Cash,Taxi Affiliation Services,1380,,0.0
10,5.85,10,1,2,1382319000,41.985015101,-87.804532006,,,0.0,,,Cash,Taxi Affiliation Services,180,,0.0
14,16.65,5,7,5,1369897200,41.968069,-87.721559063,,,0.0,,,Cash,Dispatch Taxi Affiliation,1080,,0.0
13,16.45,11,12,3,1446554700,41.983636307,-87.723583185,,,6.9,,,Cash,,780,,0.0
16,32.05,12,1,1,1417916700,41.953582125,-87.72345239,,,15.4,,,Cash,,1200,,0.0
30,38.45,10,10,5,1444301100,41.839086906,-87.714003807,,,14.6,,,Cash,,2580,,0.0
11,14.65,1,1,3,1358213400,41.978829526,-87.771166703,,,5.81,,,Cash,,1080,,0.0

Disclaimer: This site provides applications using data that has been modified for use from its original source, www.cityofchicago.org, the official website of the City of Chicago. The City of Chicago makes no claims as to the content, accuracy, timeliness, or completeness of any of the data provided at this site. The data provided at this site is subject to change at any time. It is understood that the data provided at this site is being used at one’s own risk.

Create the InteractiveContext

Last, we create an InteractiveContext, which will allow us to run TFX components interactively in this notebook.

# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()

WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/metadata.sqlite.

Run TFX components interactively

In the cells that follow, we create TFX components one-by-one, run each of them, and visualize their output artifacts.

ExampleGen

The ExampleGen component is usually at the start of a TFX pipeline. It will:

1. Split data into training and evaluation sets (by default, 2/3 training + 1/3 eval)
2. Convert data into the tf.Example format
3. Copy data into the _tfx_root directory for other components to access

ExampleGen takes as input the path to your data source. In our case, this is the _data_root path that contains the downloaded CSV.

example_gen = CsvExampleGen(input=external_input(_data_root))
context.run(example_gen)

WARNING:absl:From <ipython-input-1-2e0190c2dd16>:1: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.
Instructions for updating:
external_input is deprecated, directly pass the uri to ExampleGen.
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.
INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
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.
INFO:absl:Processing input csv data /tmp/tfx-dataif8ib649/* to TFExample.
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.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized

Let's examine the output artifacts of ExampleGen. This component produces two artifacts, training examples and evaluation examples:

artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)

["train", "eval"] /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/CsvExampleGen/examples/1

We can also take a look at the first three training examples:

# Get the URI of the output artifact representing the training examples, which is a directory
train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'Split-train')

# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
                      for name in os.listdir(train_uri)]

# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)

features {
  feature {
    key: "company"
    value {
      bytes_list {
        value: "Chicago Elite Cab Corp. (Chicago Carriag"
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 12.449999809265137
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Credit Card"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 5
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1400269500
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      bytes_list {
        value: "Taxi Affiliation Services"
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 27.049999237060547
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Cash"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
        value: 41.836151123046875
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
        value: -87.64878845214844
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 12.600000381469727
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 1380
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 10
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1380593700
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      bytes_list {
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 16.450000762939453
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Cash"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
        value: 41.98363494873047
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
        value: -87.72357940673828
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 6.900000095367432
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 780
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 11
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1446554700
      }
    }
  }
}

Now that ExampleGen has finished ingesting the data, the next step is data analysis.

StatisticsGen

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 library.

StatisticsGen takes as input the dataset we just ingested using ExampleGen.

statistics_gen = StatisticsGen(
    examples=example_gen.outputs['examples'])
context.run(statistics_gen)

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/StatisticsGen/statistics/2/Split-eval.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

After StatisticsGen finishes running, we can visualize the outputted statistics. Try playing with the different plots!

context.show(statistics_gen.outputs['statistics'])

SchemaGen

The SchemaGen component generates a schema based on your data statistics. (A schema defines the expected bounds, types, and properties of the features in your dataset.) It also uses the TensorFlow Data Validation library.

SchemaGen will take as input the statistics that we generated with StatisticsGen, looking at the training split by default.

schema_gen = SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

After SchemaGen finishes running, we can visualize the generated schema as a table.

context.show(schema_gen.outputs['schema'])

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.

To learn more about schemas, see the SchemaGen documentation.

ExampleValidator

The ExampleValidator component detects anomalies in your data, based on the expectations defined by the schema. It also uses the TensorFlow Data Validation library.

ExampleValidator will take as input the statistics from StatisticsGen, and the schema from SchemaGen.

example_validator = ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=schema_gen.outputs['schema'])
context.run(example_validator)

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for ExampleValidator
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/ExampleValidator/anomalies/4/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

After ExampleValidator finishes running, we can visualize the anomalies as a table.

context.show(example_validator.outputs['anomalies'])

/tmpfs/src/tf_docs_env/lib/python3.6/site-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.
  pd.set_option('max_colwidth', -1)

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.

Transform

The Transform component performs feature engineering for both training and serving. It uses the TensorFlow Transform library.

Transform will take as input the data from ExampleGen, the schema from SchemaGen, as well as a module that contains user-defined Transform code.

Let's see an example of user-defined Transform code below (for an introduction to the TensorFlow Transform APIs, see the tutorial). First, we define a few constants for feature engineering:

_taxi_constants_module_file = 'taxi_constants.py'

%%writefile {_taxi_constants_module_file}

# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]

CATEGORICAL_FEATURE_KEYS = [
    'trip_start_hour', 'trip_start_day', 'trip_start_month',
    'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',
    'dropoff_community_area'
]

DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']

# Number of buckets used by tf.transform for encoding each feature.
FEATURE_BUCKET_COUNT = 10

BUCKET_FEATURE_KEYS = [
    'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',
    'dropoff_longitude'
]

# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000

# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.
OOV_SIZE = 10

VOCAB_FEATURE_KEYS = [
    'payment_type',
    'company',
]

# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'

def transformed_name(key):
  return key + '_xf'

Writing taxi_constants.py

Next, we write a preprocessing_fn that takes in raw data as input, and returns transformed features that our model can train on:

_taxi_transform_module_file = 'taxi_transform.py'

%%writefile {_taxi_transform_module_file}

import tensorflow as tf
import tensorflow_transform as tft

import taxi_constants

_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_FARE_KEY = taxi_constants.FARE_KEY
_LABEL_KEY = taxi_constants.LABEL_KEY
_transformed_name = taxi_constants.transformed_name


def preprocessing_fn(inputs):
  """tf.transform's callback function for preprocessing inputs.
  Args:
    inputs: map from feature keys to raw not-yet-transformed features.
  Returns:
    Map from string feature key to transformed feature operations.
  """
  outputs = {}
  for key in _DENSE_FLOAT_FEATURE_KEYS:
    # Preserve this feature as a dense float, setting nan's to the mean.
    outputs[_transformed_name(key)] = tft.scale_to_z_score(
        _fill_in_missing(inputs[key]))

  for key in _VOCAB_FEATURE_KEYS:
    # Build a vocabulary for this feature.
    outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
        _fill_in_missing(inputs[key]),
        top_k=_VOCAB_SIZE,
        num_oov_buckets=_OOV_SIZE)

  for key in _BUCKET_FEATURE_KEYS:
    outputs[_transformed_name(key)] = tft.bucketize(
        _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)

  for key in _CATEGORICAL_FEATURE_KEYS:
    outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])

  # Was this passenger a big tipper?
  taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
  tips = _fill_in_missing(inputs[_LABEL_KEY])
  outputs[_transformed_name(_LABEL_KEY)] = tf.where(
      tf.math.is_nan(taxi_fare),
      tf.cast(tf.zeros_like(taxi_fare), tf.int64),
      # Test if the tip was > 20% of the fare.
      tf.cast(
          tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))

  return outputs


def _fill_in_missing(x):
  """Replace missing values in a SparseTensor.
  Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
  Args:
    x: A `SparseTensor` of rank 2.  Its dense shape should have size at most 1
      in the second dimension.
  Returns:
    A rank 1 tensor where missing values of `x` have been filled in.
  """
  if not isinstance(x, tf.sparse.SparseTensor):
    return x

  default_value = '' if x.dtype == tf.string else 0
  return tf.squeeze(
      tf.sparse.to_dense(
          tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
          default_value),
      axis=1)

Writing taxi_transform.py

Now, we pass in this feature engineering code to the Transform component and run it to transform your data.

transform = Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)

INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
WARNING:absl:The default value of `force_tf_compat_v1` will change in a future release from `True` to `False`. Since this pipeline has TF 2 behaviors enabled, Transform will use native TF 2 at that point. You can test this behavior now by passing `force_tf_compat_v1=False` or disable it by explicitly setting `force_tf_compat_v1=True` in the Transform component.
INFO:absl:Loading source_path /tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py as name user_module_0 because it has not been loaded before.
INFO:absl:/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py is already loaded, reloading
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/transform_graph/5/.temp_path/tftransform_tmp/6e64b14046d54c18923e3bfeb9107b12/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/transform_graph/5/.temp_path/tftransform_tmp/69524ef4330446b68df463ec89135580/saved_model.pb
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/transform_graph/5/.temp_path/tftransform_tmp/ebd2c8d68b8c4dbcacb490b94b76f6cb/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/transform_graph/5/.temp_path/tftransform_tmp/ebd2c8d68b8c4dbcacb490b94b76f6cb/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

Let's examine the output artifacts of Transform. This component produces two types of outputs:

• transform_graph is the graph that can perform the preprocessing operations (this graph will be included in the serving and evaluation models).
• transformed_examples represents the preprocessed training and evaluation data.

transform.outputs

{'transform_graph': Channel(
    type_name: TransformGraph
    artifacts: [Artifact(artifact: id: 5
type_id: 13
uri: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/transform_graph/5"
custom_properties {
  key: "name"
  value {
    string_value: "transform_graph"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Transform"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 13
name: "TransformGraph"
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'transformed_examples': Channel(
    type_name: Examples
    artifacts: [Artifact(artifact: id: 6
type_id: 5
uri: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/transformed_examples/5"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "transformed_examples"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Transform"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'updated_analyzer_cache': Channel(
    type_name: TransformCache
    artifacts: [Artifact(artifact: id: 7
type_id: 14
uri: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Transform/updated_analyzer_cache/5"
custom_properties {
  key: "name"
  value {
    string_value: "updated_analyzer_cache"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Transform"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "TransformCache"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Take a peek at the transform_graph artifact. It points to a directory containing three subdirectories.

train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)

['transform_fn', 'transformed_metadata', 'metadata']

The transformed_metadata subdirectory contains the schema of the preprocessed data. The transform_fn subdirectory contains the actual preprocessing graph. The metadata subdirectory contains the schema of the original data.

We can also take a look at the first three transformed examples:

# Get the URI of the output artifact representing the transformed examples, which is a directory
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')

# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
                      for name in os.listdir(train_uri)]

# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 8
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.061060599982738495
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: -0.15886740386486053
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: -0.7118487358093262
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 1.2521240711212158
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.532160758972168
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.5509493350982666
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 10
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.3873794376850128
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.21955278515815735
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.0019067145185545087
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 11
      }
    }
  }
}

After the Transform component has transformed your data into features, and the next step is to train a model.

Trainer

The Trainer component will train a model that you define in TensorFlow (either using the Estimator API or the Keras API with model_to_estimator).

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.

Let's see an example of user-defined model code below (for an introduction to the TensorFlow Estimator APIs, see the tutorial):

_taxi_trainer_module_file = 'taxi_trainer.py'

%%writefile {_taxi_trainer_module_file}

import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx_bsl.tfxio import dataset_options

import taxi_constants

_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_MAX_CATEGORICAL_FEATURE_VALUES = taxi_constants.MAX_CATEGORICAL_FEATURE_VALUES
_LABEL_KEY = taxi_constants.LABEL_KEY
_transformed_name = taxi_constants.transformed_name


def _transformed_names(keys):
  return [_transformed_name(key) for key in keys]


# Tf.Transform considers these features as "raw"
def _get_raw_feature_spec(schema):
  return schema_utils.schema_as_feature_spec(schema).feature_spec


def _build_estimator(config, hidden_units=None, warm_start_from=None):
  """Build an estimator for predicting the tipping behavior of taxi riders.
  Args:
    config: tf.estimator.RunConfig defining the runtime environment for the
      estimator (including model_dir).
    hidden_units: [int], the layer sizes of the DNN (input layer first)
    warm_start_from: Optional directory to warm start from.
  Returns:
    A dict of the following:

      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  real_valued_columns = [
      tf.feature_column.numeric_column(key, shape=())
      for key in _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
  ]
  categorical_columns = [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
      for key in _transformed_names(_VOCAB_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
      for key in _transformed_names(_BUCKET_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
          key,
          num_buckets=num_buckets,
          default_value=0) for key, num_buckets in zip(
              _transformed_names(_CATEGORICAL_FEATURE_KEYS),
              _MAX_CATEGORICAL_FEATURE_VALUES)
  ]
  return tf.estimator.DNNLinearCombinedClassifier(
      config=config,
      linear_feature_columns=categorical_columns,
      dnn_feature_columns=real_valued_columns,
      dnn_hidden_units=hidden_units or [100, 70, 50, 25],
      warm_start_from=warm_start_from)


def _example_serving_receiver_fn(tf_transform_graph, schema):
  """Build the serving in inputs.
  Args:
    tf_transform_graph: A TFTransformOutput.
    schema: the schema of the input data.
  Returns:
    Tensorflow graph which parses examples, applying tf-transform to them.
  """
  raw_feature_spec = _get_raw_feature_spec(schema)
  raw_feature_spec.pop(_LABEL_KEY)

  raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
      raw_feature_spec, default_batch_size=None)
  serving_input_receiver = raw_input_fn()

  transformed_features = tf_transform_graph.transform_raw_features(
      serving_input_receiver.features)

  return tf.estimator.export.ServingInputReceiver(
      transformed_features, serving_input_receiver.receiver_tensors)


def _eval_input_receiver_fn(tf_transform_graph, schema):
  """Build everything needed for the tf-model-analysis to run the model.
  Args:
    tf_transform_graph: A TFTransformOutput.
    schema: the schema of the input data.
  Returns:
    EvalInputReceiver function, which contains:

      - Tensorflow graph which parses raw untransformed features, applies the
        tf-transform preprocessing operators.
      - Set of raw, untransformed features.
      - Label against which predictions will be compared.
  """
  # Notice that the inputs are raw features, not transformed features here.
  raw_feature_spec = _get_raw_feature_spec(schema)

  serialized_tf_example = tf.compat.v1.placeholder(
      dtype=tf.string, shape=[None], name='input_example_tensor')

  # Add a parse_example operator to the tensorflow graph, which will parse
  # raw, untransformed, tf examples.
  features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)

  # Now that we have our raw examples, process them through the tf-transform
  # function computed during the preprocessing step.
  transformed_features = tf_transform_graph.transform_raw_features(
      features)

  # The key name MUST be 'examples'.
  receiver_tensors = {'examples': serialized_tf_example}

  # NOTE: Model is driven by transformed features (since training works on the
  # materialized output of TFT, but slicing will happen on raw features.
  features.update(transformed_features)

  return tfma.export.EvalInputReceiver(
      features=features,
      receiver_tensors=receiver_tensors,
      labels=transformed_features[_transformed_name(_LABEL_KEY)])


def _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size=200):
  """Generates features and label for tuning/training.

  Args:
    file_pattern: List of paths or patterns of input tfrecord files.
    data_accessor: DataAccessor for converting input to RecordBatch.
    tf_transform_output: A TFTransformOutput.
    batch_size: representing the number of consecutive elements of returned
      dataset to combine in a single batch

  Returns:
    A dataset that contains (features, indices) tuple where features is a
      dictionary of Tensors, and indices is a single Tensor of label indices.
  """
  return data_accessor.tf_dataset_factory(
      file_pattern,
      dataset_options.TensorFlowDatasetOptions(
          batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)),
      tf_transform_output.transformed_metadata.schema)


# TFX will call this function
def trainer_fn(trainer_fn_args, schema):
  """Build the estimator using the high level API.
  Args:
    trainer_fn_args: Holds args used to train the model as name/value pairs.
    schema: Holds the schema of the training examples.
  Returns:
    A dict of the following:

      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  # Number of nodes in the first layer of the DNN
  first_dnn_layer_size = 100
  num_dnn_layers = 4
  dnn_decay_factor = 0.7

  train_batch_size = 40
  eval_batch_size = 40

  tf_transform_graph = tft.TFTransformOutput(trainer_fn_args.transform_output)

  train_input_fn = lambda: _input_fn(  # pylint: disable=g-long-lambda
      trainer_fn_args.train_files,
      trainer_fn_args.data_accessor,
      tf_transform_graph,
      batch_size=train_batch_size)

  eval_input_fn = lambda: _input_fn(  # pylint: disable=g-long-lambda
      trainer_fn_args.eval_files,
      trainer_fn_args.data_accessor,
      tf_transform_graph,
      batch_size=eval_batch_size)

  train_spec = tf.estimator.TrainSpec(  # pylint: disable=g-long-lambda
      train_input_fn,
      max_steps=trainer_fn_args.train_steps)

  serving_receiver_fn = lambda: _example_serving_receiver_fn(  # pylint: disable=g-long-lambda
      tf_transform_graph, schema)

  exporter = tf.estimator.FinalExporter('chicago-taxi', serving_receiver_fn)
  eval_spec = tf.estimator.EvalSpec(
      eval_input_fn,
      steps=trainer_fn_args.eval_steps,
      exporters=[exporter],
      name='chicago-taxi-eval')

  run_config = tf.estimator.RunConfig(
      save_checkpoints_steps=999, keep_checkpoint_max=1)

  run_config = run_config.replace(model_dir=trainer_fn_args.serving_model_dir)

  estimator = _build_estimator(
      # Construct layers sizes with exponetial decay
      hidden_units=[
          max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
          for i in range(num_dnn_layers)
      ],
      config=run_config,
      warm_start_from=trainer_fn_args.base_model)

  # Create an input receiver for TFMA processing
  receiver_fn = lambda: _eval_input_receiver_fn(  # pylint: disable=g-long-lambda
      tf_transform_graph, schema)

  return {
      'estimator': estimator,
      'train_spec': train_spec,
      'eval_spec': eval_spec,
      'eval_input_receiver_fn': receiver_fn
  }

Writing taxi_trainer.py

Now, we pass in this model code to the Trainer component and run it to train the model.

trainer = Trainer(
    module_file=os.path.abspath(_taxi_trainer_module_file),
    transformed_examples=transform.outputs['transformed_examples'],
    schema=schema_gen.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000))
context.run(trainer)

INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:Loading source_path /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py as name user_module_1 because it has not been loaded before.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:absl:Training model.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1727: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  warnings.warn('`layer.add_variable` is deprecated and '
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/adagrad.py:83: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.6937907, step = 0
INFO:tensorflow:global_step/sec: 79.2029
INFO:tensorflow:loss = 0.579387, step = 100 (1.264 sec)
INFO:tensorflow:global_step/sec: 101.271
INFO:tensorflow:loss = 0.554636, step = 200 (0.988 sec)
INFO:tensorflow:global_step/sec: 99.8455
INFO:tensorflow:loss = 0.54393536, step = 300 (1.001 sec)
INFO:tensorflow:global_step/sec: 100.778
INFO:tensorflow:loss = 0.6103128, step = 400 (0.992 sec)
INFO:tensorflow:global_step/sec: 100.066
INFO:tensorflow:loss = 0.55928546, step = 500 (0.999 sec)
INFO:tensorflow:global_step/sec: 100.34
INFO:tensorflow:loss = 0.45000362, step = 600 (0.997 sec)
INFO:tensorflow:global_step/sec: 101.142
INFO:tensorflow:loss = 0.44432694, step = 700 (0.989 sec)
INFO:tensorflow:global_step/sec: 99.9258
INFO:tensorflow:loss = 0.48633447, step = 800 (1.001 sec)
INFO:tensorflow:global_step/sec: 102.245
INFO:tensorflow:loss = 0.44627285, step = 900 (0.978 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py:970: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-04-13T09:07:10Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 47.38006s
INFO:tensorflow:Finished evaluation at 2021-04-13-09:07:58
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.77126, accuracy_baseline = 0.77126, auc = 0.9163854, auc_precision_recall = 0.64540094, average_loss = 0.4622827, global_step = 999, label/mean = 0.22874, loss = 0.4622833, precision = 0.0, prediction/mean = 0.24699566, recall = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 1.99565
INFO:tensorflow:loss = 0.4235708, step = 1000 (50.108 sec)
INFO:tensorflow:global_step/sec: 103.862
INFO:tensorflow:loss = 0.5051597, step = 1100 (0.963 sec)
INFO:tensorflow:global_step/sec: 103.964
INFO:tensorflow:loss = 0.42090735, step = 1200 (0.962 sec)
INFO:tensorflow:global_step/sec: 104.451
INFO:tensorflow:loss = 0.4491743, step = 1300 (0.957 sec)
INFO:tensorflow:global_step/sec: 105.566
INFO:tensorflow:loss = 0.40952644, step = 1400 (0.947 sec)
INFO:tensorflow:global_step/sec: 104.461
INFO:tensorflow:loss = 0.44348988, step = 1500 (0.957 sec)
INFO:tensorflow:global_step/sec: 104.38
INFO:tensorflow:loss = 0.52951086, step = 1600 (0.958 sec)
INFO:tensorflow:global_step/sec: 104.702
INFO:tensorflow:loss = 0.38752165, step = 1700 (0.955 sec)
INFO:tensorflow:global_step/sec: 104.238
INFO:tensorflow:loss = 0.5325674, step = 1800 (0.959 sec)
INFO:tensorflow:global_step/sec: 105.445
INFO:tensorflow:loss = 0.48275957, step = 1900 (0.949 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 93.1032
INFO:tensorflow:loss = 0.383391, step = 2000 (1.074 sec)
INFO:tensorflow:global_step/sec: 105.719
INFO:tensorflow:loss = 0.48600692, step = 2100 (0.946 sec)
INFO:tensorflow:global_step/sec: 105.383
INFO:tensorflow:loss = 0.44580156, step = 2200 (0.950 sec)
INFO:tensorflow:global_step/sec: 104.789
INFO:tensorflow:loss = 0.4048962, step = 2300 (0.953 sec)
INFO:tensorflow:global_step/sec: 104.325
INFO:tensorflow:loss = 0.35834712, step = 2400 (0.959 sec)
INFO:tensorflow:global_step/sec: 105.762
INFO:tensorflow:loss = 0.43977723, step = 2500 (0.946 sec)
INFO:tensorflow:global_step/sec: 105.299
INFO:tensorflow:loss = 0.45189613, step = 2600 (0.950 sec)
INFO:tensorflow:global_step/sec: 105.109
INFO:tensorflow:loss = 0.49688178, step = 2700 (0.951 sec)
INFO:tensorflow:global_step/sec: 103.808
INFO:tensorflow:loss = 0.29522616, step = 2800 (0.963 sec)
INFO:tensorflow:global_step/sec: 104.345
INFO:tensorflow:loss = 0.4492773, step = 2900 (0.959 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 91.8495
INFO:tensorflow:loss = 0.4269491, step = 3000 (1.089 sec)
INFO:tensorflow:global_step/sec: 102.801
INFO:tensorflow:loss = 0.30682844, step = 3100 (0.973 sec)
INFO:tensorflow:global_step/sec: 104.269
INFO:tensorflow:loss = 0.4384345, step = 3200 (0.959 sec)
INFO:tensorflow:global_step/sec: 104.223
INFO:tensorflow:loss = 0.45694605, step = 3300 (0.960 sec)
INFO:tensorflow:global_step/sec: 104.322
INFO:tensorflow:loss = 0.3415828, step = 3400 (0.958 sec)
INFO:tensorflow:global_step/sec: 103.319
INFO:tensorflow:loss = 0.37819222, step = 3500 (0.968 sec)
INFO:tensorflow:global_step/sec: 102.964
INFO:tensorflow:loss = 0.50913537, step = 3600 (0.971 sec)
INFO:tensorflow:global_step/sec: 104.976
INFO:tensorflow:loss = 0.34711474, step = 3700 (0.953 sec)
INFO:tensorflow:global_step/sec: 105.746
INFO:tensorflow:loss = 0.38880587, step = 3800 (0.946 sec)
INFO:tensorflow:global_step/sec: 103.548
INFO:tensorflow:loss = 0.45630985, step = 3900 (0.966 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 92.6067
INFO:tensorflow:loss = 0.50493205, step = 4000 (1.080 sec)
INFO:tensorflow:global_step/sec: 104.025
INFO:tensorflow:loss = 0.37739924, step = 4100 (0.961 sec)
INFO:tensorflow:global_step/sec: 104.037
INFO:tensorflow:loss = 0.40609327, step = 4200 (0.961 sec)
INFO:tensorflow:global_step/sec: 105.412
INFO:tensorflow:loss = 0.3691474, step = 4300 (0.949 sec)
INFO:tensorflow:global_step/sec: 105.208
INFO:tensorflow:loss = 0.47721606, step = 4400 (0.951 sec)
INFO:tensorflow:global_step/sec: 101.635
INFO:tensorflow:loss = 0.40973035, step = 4500 (0.984 sec)
INFO:tensorflow:global_step/sec: 102.573
INFO:tensorflow:loss = 0.44837776, step = 4600 (0.975 sec)
INFO:tensorflow:global_step/sec: 104.413
INFO:tensorflow:loss = 0.3838393, step = 4700 (0.958 sec)
INFO:tensorflow:global_step/sec: 105.916
INFO:tensorflow:loss = 0.44911927, step = 4800 (0.944 sec)
INFO:tensorflow:global_step/sec: 103.681
INFO:tensorflow:loss = 0.46360612, step = 4900 (0.965 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 92.3541
INFO:tensorflow:loss = 0.41456, step = 5000 (1.083 sec)
INFO:tensorflow:global_step/sec: 103.19
INFO:tensorflow:loss = 0.36265916, step = 5100 (0.969 sec)
INFO:tensorflow:global_step/sec: 104.588
INFO:tensorflow:loss = 0.3077788, step = 5200 (0.956 sec)
INFO:tensorflow:global_step/sec: 104.682
INFO:tensorflow:loss = 0.35692006, step = 5300 (0.955 sec)
INFO:tensorflow:global_step/sec: 105.658
INFO:tensorflow:loss = 0.44540375, step = 5400 (0.946 sec)
INFO:tensorflow:global_step/sec: 105.031
INFO:tensorflow:loss = 0.30228376, step = 5500 (0.952 sec)
INFO:tensorflow:global_step/sec: 101.035
INFO:tensorflow:loss = 0.2782836, step = 5600 (0.990 sec)
INFO:tensorflow:global_step/sec: 103.921
INFO:tensorflow:loss = 0.39522114, step = 5700 (0.962 sec)
INFO:tensorflow:global_step/sec: 103.961
INFO:tensorflow:loss = 0.3037942, step = 5800 (0.962 sec)
INFO:tensorflow:global_step/sec: 103.764
INFO:tensorflow:loss = 0.370727, step = 5900 (0.964 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 89.8389
INFO:tensorflow:loss = 0.43186942, step = 6000 (1.113 sec)
INFO:tensorflow:global_step/sec: 102.285
INFO:tensorflow:loss = 0.33156097, step = 6100 (0.978 sec)
INFO:tensorflow:global_step/sec: 103.405
INFO:tensorflow:loss = 0.2887263, step = 6200 (0.967 sec)
INFO:tensorflow:global_step/sec: 103.171
INFO:tensorflow:loss = 0.3552881, step = 6300 (0.969 sec)
INFO:tensorflow:global_step/sec: 101.446
INFO:tensorflow:loss = 0.3368496, step = 6400 (0.986 sec)
INFO:tensorflow:global_step/sec: 103.096
INFO:tensorflow:loss = 0.37829036, step = 6500 (0.970 sec)
INFO:tensorflow:global_step/sec: 102.98
INFO:tensorflow:loss = 0.39961675, step = 6600 (0.971 sec)
INFO:tensorflow:global_step/sec: 103.337
INFO:tensorflow:loss = 0.38277107, step = 6700 (0.968 sec)
INFO:tensorflow:global_step/sec: 103.335
INFO:tensorflow:loss = 0.42959777, step = 6800 (0.967 sec)
INFO:tensorflow:global_step/sec: 101.19
INFO:tensorflow:loss = 0.40601236, step = 6900 (0.988 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 89.4705
INFO:tensorflow:loss = 0.38863873, step = 7000 (1.118 sec)
INFO:tensorflow:global_step/sec: 102.425
INFO:tensorflow:loss = 0.40610886, step = 7100 (0.976 sec)
INFO:tensorflow:global_step/sec: 104.421
INFO:tensorflow:loss = 0.2921072, step = 7200 (0.958 sec)
INFO:tensorflow:global_step/sec: 105.022
INFO:tensorflow:loss = 0.2990289, step = 7300 (0.952 sec)
INFO:tensorflow:global_step/sec: 104.182
INFO:tensorflow:loss = 0.31598052, step = 7400 (0.960 sec)
INFO:tensorflow:global_step/sec: 103.135
INFO:tensorflow:loss = 0.4204064, step = 7500 (0.970 sec)
INFO:tensorflow:global_step/sec: 103.942
INFO:tensorflow:loss = 0.4559477, step = 7600 (0.962 sec)
INFO:tensorflow:global_step/sec: 104.163
INFO:tensorflow:loss = 0.33235478, step = 7700 (0.960 sec)
INFO:tensorflow:global_step/sec: 102.798
INFO:tensorflow:loss = 0.39219013, step = 7800 (0.973 sec)
INFO:tensorflow:global_step/sec: 102.615
INFO:tensorflow:loss = 0.34676856, step = 7900 (0.974 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 92.9641
INFO:tensorflow:loss = 0.35397115, step = 8000 (1.077 sec)
INFO:tensorflow:global_step/sec: 101.766
INFO:tensorflow:loss = 0.3400356, step = 8100 (0.982 sec)
INFO:tensorflow:global_step/sec: 104.128
INFO:tensorflow:loss = 0.3419579, step = 8200 (0.960 sec)
INFO:tensorflow:global_step/sec: 99.4881
INFO:tensorflow:loss = 0.38243654, step = 8300 (1.005 sec)
INFO:tensorflow:global_step/sec: 97.5307
INFO:tensorflow:loss = 0.40747413, step = 8400 (1.025 sec)
INFO:tensorflow:global_step/sec: 97.8186
INFO:tensorflow:loss = 0.29059124, step = 8500 (1.022 sec)
INFO:tensorflow:global_step/sec: 94.1895
INFO:tensorflow:loss = 0.36739475, step = 8600 (1.062 sec)
INFO:tensorflow:global_step/sec: 95.8142
INFO:tensorflow:loss = 0.33413044, step = 8700 (1.044 sec)
INFO:tensorflow:global_step/sec: 99.1286
INFO:tensorflow:loss = 0.46180314, step = 8800 (1.009 sec)
INFO:tensorflow:global_step/sec: 100.329
INFO:tensorflow:loss = 0.32250133, step = 8900 (0.997 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 88.3769
INFO:tensorflow:loss = 0.32691297, step = 9000 (1.131 sec)
INFO:tensorflow:global_step/sec: 100.778
INFO:tensorflow:loss = 0.31804866, step = 9100 (0.993 sec)
INFO:tensorflow:global_step/sec: 103.437
INFO:tensorflow:loss = 0.33268934, step = 9200 (0.967 sec)
INFO:tensorflow:global_step/sec: 104.303
INFO:tensorflow:loss = 0.35497054, step = 9300 (0.959 sec)
INFO:tensorflow:global_step/sec: 104.717
INFO:tensorflow:loss = 0.34016633, step = 9400 (0.955 sec)
INFO:tensorflow:global_step/sec: 103.07
INFO:tensorflow:loss = 0.44034988, step = 9500 (0.970 sec)
INFO:tensorflow:global_step/sec: 100.989
INFO:tensorflow:loss = 0.33347645, step = 9600 (0.990 sec)
INFO:tensorflow:global_step/sec: 99.8172
INFO:tensorflow:loss = 0.27859312, step = 9700 (1.002 sec)
INFO:tensorflow:global_step/sec: 99.4119
INFO:tensorflow:loss = 0.34608266, step = 9800 (1.006 sec)
INFO:tensorflow:global_step/sec: 97.2722
INFO:tensorflow:loss = 0.2646464, step = 9900 (1.028 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-04-13T09:09:28Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 46.97150s
INFO:tensorflow:Finished evaluation at 2021-04-13-09:10:15
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.78797, accuracy_baseline = 0.77126, auc = 0.9339527, auc_precision_recall = 0.7066542, average_loss = 0.34490004, global_step = 10000, label/mean = 0.22874, loss = 0.3448999, precision = 0.6933133, prediction/mean = 0.23062998, recall = 0.13100027
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1618305015/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1618305015/saved_model.pb
INFO:tensorflow:Loss for final step: 0.2904299.
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6
INFO:absl:Exporting eval_savedmodel for TFMA.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-TFMA/temp-1618305016/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-TFMA/temp-1618305016/saved_model.pb
INFO:absl:Exported eval_savedmodel to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model_run/6/Format-TFMA.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/serving_model_dir/saved_model.pb"
INFO:absl:Serving model copied to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model/6/Format-Serving.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/eval_model_dir/saved_model.pb"
INFO:absl:Eval model copied to: /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model/6/Format-TFMA.
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

Analyze Training with TensorBoard

Optionally, we can connect TensorBoard to the Trainer to analyze our model's training curves.

# Get the URI of the output artifact representing the training logs, which is a directory
model_run_dir = trainer.outputs['model_run'].get()[0].uri

%load_ext tensorboard
%tensorboard --logdir {model_run_dir}

Evaluator

The Evaluator component computes model performance metrics over the evaluation set. It uses the TensorFlow Model Analysis library. The Evaluator can also optionally validate that a newly trained model is better than the previous model. This is useful in a production pipeline setting where you may automatically train and validate a model every day. In this notebook, we only train one model, so the Evaluator automatically will label the model as "good".

Evaluator will take as input the data from ExampleGen, the trained model from Trainer, and slicing configuration. The slicing configuration allows you to slice your metrics on feature values (e.g. how does your model perform on taxi trips that start at 8am versus 8pm?). See an example of this configuration below:

eval_config = tfma.EvalConfig(
    model_specs=[
        # Using signature 'eval' implies the use of an EvalSavedModel. To use
        # a serving model remove the signature to defaults to 'serving_default'
        # and add a label_key.
        tfma.ModelSpec(signature_name='eval')
    ],
    metrics_specs=[
        tfma.MetricsSpec(
            # The metrics added here are in addition to those saved with the
            # model (assuming either a keras model or EvalSavedModel is used).
            # Any metrics added into the saved model (for example using
            # model.compile(..., metrics=[...]), etc) will be computed
            # automatically.
            metrics=[
                tfma.MetricConfig(class_name='ExampleCount')
            ],
            # To add validation thresholds for metrics saved with the model,
            # add them keyed by metric name to the thresholds map.
            thresholds = {
                'accuracy': tfma.MetricThreshold(
                    value_threshold=tfma.GenericValueThreshold(
                        lower_bound={'value': 0.5}),
                    # Change threshold will be ignored if there is no
                    # baseline model resolved from MLMD (first run).
                    change_threshold=tfma.GenericChangeThreshold(
                       direction=tfma.MetricDirection.HIGHER_IS_BETTER,
                       absolute={'value': -1e-10}))
            }
        )
    ],
    slicing_specs=[
        # An empty slice spec means the overall slice, i.e. the whole dataset.
        tfma.SlicingSpec(),
        # Data can be sliced along a feature column. In this case, data is
        # sliced along feature column trip_start_hour.
        tfma.SlicingSpec(feature_keys=['trip_start_hour'])
    ])

Next, we give this configuration to Evaluator and run it.

# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.

# The model resolver is only required if performing model validation in addition
# to evaluation. In this case we validate against the latest blessed model. If
# no model has been blessed before (as in this case) the evaluator will make our
# candidate the first blessed model.
model_resolver = ResolverNode(
      instance_name='latest_blessed_model_resolver',
      resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,
      model=Channel(type=Model),
      model_blessing=Channel(type=ModelBlessing))
context.run(model_resolver)

evaluator = Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    #baseline_model=model_resolver.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)

WARNING:absl:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/components/common_nodes/resolver_node.py:74: The name tfx.components.common_nodes.resolver_node.ResolverNode is deprecated. Please use tfx.dsl.components.common.resolver.Resolver instead.
WARNING:absl:`instance_name` is deprecated, please set the node id directly using `with_id()` or the `.id` setter.
INFO:absl:Running driver for Resolver.latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for Resolver.latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Evaluator
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Using /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model/6/Format-TFMA as  model.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:169: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model/6/Format-TFMA/variables/variables
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:189: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

Now let's examine the output artifacts of Evaluator.

evaluator.outputs

{'evaluation': Channel(
    type_name: ModelEvaluation
    artifacts: [Artifact(artifact: id: 10
type_id: 20
uri: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Evaluator/evaluation/8"
custom_properties {
  key: "name"
  value {
    string_value: "evaluation"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Evaluator"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 20
name: "ModelEvaluation"
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'blessing': Channel(
    type_name: ModelBlessing
    artifacts: [Artifact(artifact: id: 11
type_id: 21
uri: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Evaluator/blessing/8"
custom_properties {
  key: "blessed"
  value {
    int_value: 1
  }
}
custom_properties {
  key: "current_model"
  value {
    string_value: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Trainer/model/6"
  }
}
custom_properties {
  key: "current_model_id"
  value {
    int_value: 8
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "blessing"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Evaluator"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 21
name: "ModelBlessing"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Using the evaluation output we can show the default visualization of global metrics on the entire evaluation set.

context.show(evaluator.outputs['evaluation'])

To see the visualization for sliced evaluation metrics, we can directly call the TensorFlow Model Analysis library.

import tensorflow_model_analysis as tfma

# Get the TFMA output result path and load the result.
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
tfma_result = tfma.load_eval_result(PATH_TO_RESULT)

# Show data sliced along feature column trip_start_hour.
tfma.view.render_slicing_metrics(
    tfma_result, slicing_column='trip_start_hour')

SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'trip_start_hour:19',…

This visualization shows the same metrics, but computed at every feature value of trip_start_hour instead of on the entire evaluation set.

TensorFlow Model Analysis supports many other visualizations, such as Fairness Indicators and plotting a time series of model performance. To learn more, see the tutorial.

Since we added thresholds to our config, validation output is also available. The precence of a blessing artifact indicates that our model passed validation. Since this is the first validation being performed the candidate is automatically blessed.

blessing_uri = evaluator.outputs.blessing.get()[0].uri
!ls -l {blessing_uri}

total 0
-rw-rw-r-- 1 kbuilder kbuilder 0 Apr 13 09:10 BLESSED

Now can also verify the success by loading the validation result record:

PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
print(tfma.load_validation_result(PATH_TO_RESULT))

validation_ok: true
validation_details {
  slicing_details {
    slicing_spec {
    }
    num_matching_slices: 25
  }
}

Pusher

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.

pusher = Pusher(
    model=trainer.outputs['model'],
    model_blessing=evaluator.outputs['blessing'],
    push_destination=pusher_pb2.PushDestination(
        filesystem=pusher_pb2.PushDestination.Filesystem(
            base_directory=_serving_model_dir)))
context.run(pusher)

INFO:absl:Running driver for Pusher
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1618305031
INFO:absl:Model written to serving path /tmp/tmpasnbc4m_/serving_model/taxi_simple/1618305031.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

Let's examine the output artifacts of Pusher.

pusher.outputs

{'pushed_model': Channel(
    type_name: PushedModel
    artifacts: [Artifact(artifact: id: 12
type_id: 23
uri: "/tmp/tfx-interactive-2021-04-13T09_06_24.150104-azazhzcs/Pusher/pushed_model/9"
custom_properties {
  key: "name"
  value {
    string_value: "pushed_model"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Pusher"
  }
}
custom_properties {
  key: "pushed"
  value {
    int_value: 1
  }
}
custom_properties {
  key: "pushed_destination"
  value {
    string_value: "/tmp/tmpasnbc4m_/serving_model/taxi_simple/1618305031"
  }
}
custom_properties {
  key: "pushed_version"
  value {
    string_value: "1618305031"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 23
name: "PushedModel"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

In particular, the Pusher will export your model in the SavedModel format, which looks like this:

push_uri = pusher.outputs['pushed_model'].get()[0].uri
model = tf.saved_model.load(push_uri)

for item in model.signatures.items():
  pp.pprint(item)

('predict', <ConcreteFunction pruned(examples) at 0x7F24F0240780>)
('classification', <ConcreteFunction pruned(inputs) at 0x7F25F77618D0>)
('serving_default', <ConcreteFunction pruned(inputs) at 0x7F25F739D8D0>)
('regression', <ConcreteFunction pruned(inputs) at 0x7F24E07EE390>)

We're finished our tour of built-in TFX components!