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Ingeniería de funciones con TFX Pipeline y TensorFlow Transform

Transforme los datos de entrada y el seguimiento de un modelo con una canalización TFX.

En este tutorial basado en notebook, crearemos y ejecutaremos una canalización TFX para ingerir datos de entrada sin procesar y preprocesarlos adecuadamente para el entrenamiento de AA. Este portátil se basa en la tubería TFX hemos construido en la validación de datos utilizando TFX Pipeline y TensorFlow validación de datos Tutorial . Si aún no lo ha leído, debe leerlo antes de continuar con este cuaderno.

Puede aumentar la calidad predictiva de sus datos y / o reducir la dimensionalidad con la ingeniería de funciones. Uno de los beneficios de usar TFX es que escribirá su código de transformación una vez, y las transformaciones resultantes serán consistentes entre el entrenamiento y el servicio para evitar sesgos de entrenamiento / servicio.

Vamos a añadir un Transform componente de la tubería. El componente de transformación se lleva a cabo mediante el tf.transform biblioteca.

Por favor, vea Comprender TFX Tuberías para aprender más acerca de los diversos conceptos en TFX.

Configurar

Primero necesitamos instalar el paquete TFX Python y descargar el conjunto de datos que usaremos para nuestro modelo.

Actualizar Pip

Para evitar actualizar Pip en un sistema cuando se ejecuta localmente, verifique que estemos ejecutando en Colab. Por supuesto, los sistemas locales se pueden actualizar por separado.

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

Instalar TFX

pip install -U tfx

¿Reinició el tiempo de ejecución?

Si está utilizando Google Colab, la primera vez que ejecuta la celda anterior, debe reiniciar el tiempo de ejecución haciendo clic sobre el botón "RESTART RUNTIME" o usando el menú "Runtime> Restart runtime ...". Esto se debe a la forma en que Colab carga los paquetes.

Consulta las versiones de TensorFlow y TFX.

import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.6.2
TFX version: 1.3.3

Configurar variables

Hay algunas variables que se utilizan para definir una canalización. Puede personalizar estas variables como desee. De forma predeterminada, todos los resultados de la canalización se generarán en el directorio actual.

import os

PIPELINE_NAME = "penguin-transform"

# Output directory to store artifacts generated from the pipeline.
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')
# Output directory where created models from the pipeline will be exported.
SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)

from absl import logging
logging.set_verbosity(logging.INFO)  # Set default logging level.

Preparar datos de ejemplo

Descargaremos el conjunto de datos de ejemplo para usarlo en nuestra canalización TFX. El conjunto de datos que estamos utilizando es Palmer Pingüinos conjunto de datos .

Sin embargo, a diferencia de los tutoriales anteriores, que utilizan un conjunto de datos ya preprocesado, vamos a utilizar el conjunto de datos Palmer Pingüinos prima.

Debido a que el componente TFX ExampleGen lee las entradas de un directorio, necesitamos crear un directorio y copiar el conjunto de datos en él.

import urllib.request
import tempfile

DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data')  # Create a temporary directory.
_data_path = 'https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins_size.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_path, _data_filepath)
('/tmp/tfx-data22duh_39/data.csv', <http.client.HTTPMessage at 0x7fd40dfad910>)

Eche un vistazo rápido a cómo se ven los datos sin procesar.

head {_data_filepath}
species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex
Adelie,Torgersen,39.1,18.7,181,3750,MALE
Adelie,Torgersen,39.5,17.4,186,3800,FEMALE
Adelie,Torgersen,40.3,18,195,3250,FEMALE
Adelie,Torgersen,NA,NA,NA,NA,NA
Adelie,Torgersen,36.7,19.3,193,3450,FEMALE
Adelie,Torgersen,39.3,20.6,190,3650,MALE
Adelie,Torgersen,38.9,17.8,181,3625,FEMALE
Adelie,Torgersen,39.2,19.6,195,4675,MALE
Adelie,Torgersen,34.1,18.1,193,3475,NA

Hay algunas entradas con valores que están representados como desaparecidas NA . Simplemente eliminaremos esas entradas en este tutorial.

sed -i '/\bNA\b/d' {_data_filepath}
head {_data_filepath}
species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex
Adelie,Torgersen,39.1,18.7,181,3750,MALE
Adelie,Torgersen,39.5,17.4,186,3800,FEMALE
Adelie,Torgersen,40.3,18,195,3250,FEMALE
Adelie,Torgersen,36.7,19.3,193,3450,FEMALE
Adelie,Torgersen,39.3,20.6,190,3650,MALE
Adelie,Torgersen,38.9,17.8,181,3625,FEMALE
Adelie,Torgersen,39.2,19.6,195,4675,MALE
Adelie,Torgersen,41.1,17.6,182,3200,FEMALE
Adelie,Torgersen,38.6,21.2,191,3800,MALE

Debería poder ver siete características que describen a los pingüinos. Usaremos el mismo conjunto de características que los tutoriales anteriores: 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g', y predeciremos la 'especie' de un pingüino.

La única diferencia será que los datos de entrada no están preprocesados. Tenga en cuenta que no usaremos otras funciones como 'isla' o 'sexo' en este tutorial.

Preparar un archivo de esquema

Como se describe en la validación de datos utilizando TFX Pipeline y TensorFlow validación de datos Tutorial , necesitamos un archivo de esquema para el conjunto de datos. Debido a que el conjunto de datos es diferente del tutorial anterior, necesitamos generarlo nuevamente. En este tutorial, omitiremos esos pasos y solo usaremos un archivo de esquema preparado.

import shutil

SCHEMA_PATH = 'schema'

_schema_uri = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/schema/raw/schema.pbtxt'
_schema_filename = 'schema.pbtxt'
_schema_filepath = os.path.join(SCHEMA_PATH, _schema_filename)

os.makedirs(SCHEMA_PATH, exist_ok=True)
urllib.request.urlretrieve(_schema_uri, _schema_filepath)
('schema/schema.pbtxt', <http.client.HTTPMessage at 0x7fd40dfb3150>)

Este archivo de esquema se creó con la misma canalización que en el tutorial anterior sin ningún cambio manual.

Crea una canalización

Las canalizaciones TFX se definen mediante las API de Python. Vamos a añadir Transform componente de la tubería que creamos en el tutorial de validación de datos .

Un componente de transformación requiere datos de entrada procedentes de un ExampleGen componente y un esquema de un SchemaGen componente, y produce una "transformada gráfico". La salida será utilizado en un Trainer componente. Transform puede producir opcionalmente "datos transformados" además, que son los datos materializados después de la transformación. Sin embargo, transformaremos los datos durante el entrenamiento en este tutorial sin materializar los datos transformados intermedios.

Una cosa a destacar es que tenemos que definir una función de Python, preprocessing_fn para describir cómo se deben transformar los datos de entrada. Esto es similar a un componente Trainer que también requiere un código de usuario para la definición del modelo.

Escribir código de preprocesamiento y entrenamiento

Necesitamos definir dos funciones de Python. Uno para Transform y otro para Trainer.

preprocessing_fn

El componente Transformar encontrará una función llamada preprocessing_fn en el archivo de módulo dado como lo hicimos para Trainer componente. También puede especificar una función específica mediante el preprocessing_fn parámetro del componente Transformar.

En este ejemplo, haremos dos tipos de transformación. Para características numéricos continuos como culmen_length_mm y body_mass_g , vamos a normalizar estos valores utilizando el tft.scale_to_z_score función. Para la función de etiqueta, necesitamos convertir etiquetas de cadena en valores de índice numérico. Vamos a utilizar tf.lookup.StaticHashTable para la conversión.

Para identificar los campos transformados fácilmente, añadimos una _xf sufijo a los nombres de características transformadas.

run_fn

El modelo en sí es casi el mismo que en los tutoriales anteriores, pero esta vez transformaremos los datos de entrada usando el gráfico de transformación del componente Transformar.

Una diferencia más importante en comparación con el tutorial anterior es que ahora exportamos un modelo para servir que incluye no solo el gráfico de cálculo del modelo, sino también el gráfico de transformación para preprocesamiento, que se genera en el componente Transformar. Necesitamos definir una función separada que se utilizará para atender las solicitudes entrantes. Se puede ver que la misma función _apply_preprocessing se utilizó para ambos los datos de entrenamiento y la solicitud de servir.

_module_file = 'penguin_utils.py'
%%writefile {_module_file}


from typing import List, Text
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_metadata.proto.v0 import schema_pb2
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils

from tfx import v1 as tfx
from tfx_bsl.public import tfxio

# Specify features that we will use.
_FEATURE_KEYS = [
    'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'

_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10


# NEW: TFX Transform will call this function.
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.
  """
  outputs = {}

  # Uses features defined in _FEATURE_KEYS only.
  for key in _FEATURE_KEYS:
    # tft.scale_to_z_score computes the mean and variance of the given feature
    # and scales the output based on the result.
    outputs[key] = tft.scale_to_z_score(inputs[key])

  # For the label column we provide the mapping from string to index.
  # We could instead use `tft.compute_and_apply_vocabulary()` in order to
  # compute the vocabulary dynamically and perform a lookup.
  # Since in this example there are only 3 possible values, we use a hard-coded
  # table for simplicity.
  table_keys = ['Adelie', 'Chinstrap', 'Gentoo']
  initializer = tf.lookup.KeyValueTensorInitializer(
      keys=table_keys,
      values=tf.cast(tf.range(len(table_keys)), tf.int64),
      key_dtype=tf.string,
      value_dtype=tf.int64)
  table = tf.lookup.StaticHashTable(initializer, default_value=-1)
  outputs[_LABEL_KEY] = table.lookup(inputs[_LABEL_KEY])

  return outputs


# NEW: This function will apply the same transform operation to training data
#      and serving requests.
def _apply_preprocessing(raw_features, tft_layer):
  transformed_features = tft_layer(raw_features)
  if _LABEL_KEY in raw_features:
    transformed_label = transformed_features.pop(_LABEL_KEY)
    return transformed_features, transformed_label
  else:
    return transformed_features, None


# NEW: This function will create a handler function which gets a serialized
#      tf.example, preprocess and run an inference with it.
def _get_serve_tf_examples_fn(model, tf_transform_output):
  # We must save the tft_layer to the model to ensure its assets are kept and
  # tracked.
  model.tft_layer = tf_transform_output.transform_features_layer()

  @tf.function(input_signature=[
      tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
  ])
  def serve_tf_examples_fn(serialized_tf_examples):
    # Expected input is a string which is serialized tf.Example format.
    feature_spec = tf_transform_output.raw_feature_spec()
    # Because input schema includes unnecessary fields like 'species' and
    # 'island', we filter feature_spec to include required keys only.
    required_feature_spec = {
        k: v for k, v in feature_spec.items() if k in _FEATURE_KEYS
    }
    parsed_features = tf.io.parse_example(serialized_tf_examples,
                                          required_feature_spec)

    # Preprocess parsed input with transform operation defined in
    # preprocessing_fn().
    transformed_features, _ = _apply_preprocessing(parsed_features,
                                                   model.tft_layer)
    # Run inference with ML model.
    return model(transformed_features)

  return serve_tf_examples_fn


def _input_fn(file_pattern: List[Text],
              data_accessor: tfx.components.DataAccessor,
              tf_transform_output: tft.TFTransformOutput,
              batch_size: int = 200) -> tf.data.Dataset:
  """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.
  """
  dataset = data_accessor.tf_dataset_factory(
      file_pattern,
      tfxio.TensorFlowDatasetOptions(batch_size=batch_size),
      schema=tf_transform_output.raw_metadata.schema)

  transform_layer = tf_transform_output.transform_features_layer()
  def apply_transform(raw_features):
    return _apply_preprocessing(raw_features, transform_layer)

  return dataset.map(apply_transform).repeat()


def _build_keras_model() -> tf.keras.Model:
  """Creates a DNN Keras model for classifying penguin data.

  Returns:
    A Keras Model.
  """
  # The model below is built with Functional API, please refer to
  # https://www.tensorflow.org/guide/keras/overview for all API options.
  inputs = [
      keras.layers.Input(shape=(1,), name=key)
      for key in _FEATURE_KEYS
  ]
  d = keras.layers.concatenate(inputs)
  for _ in range(2):
    d = keras.layers.Dense(8, activation='relu')(d)
  outputs = keras.layers.Dense(3)(d)

  model = keras.Model(inputs=inputs, outputs=outputs)
  model.compile(
      optimizer=keras.optimizers.Adam(1e-2),
      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=[keras.metrics.SparseCategoricalAccuracy()])

  model.summary(print_fn=logging.info)
  return model


# TFX Trainer will call this function.
def run_fn(fn_args: tfx.components.FnArgs):
  """Train the model based on given args.

  Args:
    fn_args: Holds args used to train the model as name/value pairs.
  """
  tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)

  train_dataset = _input_fn(
      fn_args.train_files,
      fn_args.data_accessor,
      tf_transform_output,
      batch_size=_TRAIN_BATCH_SIZE)
  eval_dataset = _input_fn(
      fn_args.eval_files,
      fn_args.data_accessor,
      tf_transform_output,
      batch_size=_EVAL_BATCH_SIZE)

  model = _build_keras_model()
  model.fit(
      train_dataset,
      steps_per_epoch=fn_args.train_steps,
      validation_data=eval_dataset,
      validation_steps=fn_args.eval_steps)

  # NEW: Save a computation graph including transform layer.
  signatures = {
      'serving_default': _get_serve_tf_examples_fn(model, tf_transform_output),
  }
  model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing penguin_utils.py

Ahora ha completado todos los pasos de preparación para construir una canalización TFX.

Escribe una definición de canalización

Definimos una función para crear una tubería TFX. A Pipeline objeto representa una tubería TFX, que se puede ejecutar usando uno de los sistemas de tubería de orquestación que soporta TFX.

def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
                     schema_path: str, module_file: str, serving_model_dir: str,
                     metadata_path: str) -> tfx.dsl.Pipeline:
  """Implements the penguin pipeline with TFX."""
  # Brings data into the pipeline or otherwise joins/converts training data.
  example_gen = tfx.components.CsvExampleGen(input_base=data_root)

  # Computes statistics over data for visualization and example validation.
  statistics_gen = tfx.components.StatisticsGen(
      examples=example_gen.outputs['examples'])

  # Import the schema.
  schema_importer = tfx.dsl.Importer(
      source_uri=schema_path,
      artifact_type=tfx.types.standard_artifacts.Schema).with_id(
          'schema_importer')

  # Performs anomaly detection based on statistics and data schema.
  example_validator = tfx.components.ExampleValidator(
      statistics=statistics_gen.outputs['statistics'],
      schema=schema_importer.outputs['result'])

  # NEW: Transforms input data using preprocessing_fn in the 'module_file'.
  transform = tfx.components.Transform(
      examples=example_gen.outputs['examples'],
      schema=schema_importer.outputs['result'],
      materialize=False,
      module_file=module_file)

  # Uses user-provided Python function that trains a model.
  trainer = tfx.components.Trainer(
      module_file=module_file,
      examples=example_gen.outputs['examples'],

      # NEW: Pass transform_graph to the trainer.
      transform_graph=transform.outputs['transform_graph'],

      train_args=tfx.proto.TrainArgs(num_steps=100),
      eval_args=tfx.proto.EvalArgs(num_steps=5))

  # Pushes the model to a filesystem destination.
  pusher = tfx.components.Pusher(
      model=trainer.outputs['model'],
      push_destination=tfx.proto.PushDestination(
          filesystem=tfx.proto.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

  components = [
      example_gen,
      statistics_gen,
      schema_importer,
      example_validator,

      transform,  # NEW: Transform component was added to the pipeline.

      trainer,
      pusher,
  ]

  return tfx.dsl.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      metadata_connection_config=tfx.orchestration.metadata
      .sqlite_metadata_connection_config(metadata_path),
      components=components)

Ejecutar la tubería

Vamos a utilizar LocalDagRunner como en el tutorial anterior.

tfx.orchestration.LocalDagRunner().run(
  _create_pipeline(
      pipeline_name=PIPELINE_NAME,
      pipeline_root=PIPELINE_ROOT,
      data_root=DATA_ROOT,
      schema_path=SCHEMA_PATH,
      module_file=_module_file,
      serving_model_dir=SERVING_MODEL_DIR,
      metadata_path=METADATA_PATH))
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_utils.py' (including modules: ['penguin_utils']).
INFO:absl:User module package has hash fingerprint version a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmptoo9orbn/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpibb6gqep', '--dist-dir', '/tmp/tmpm0e9w5ft']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'; target user module is 'penguin_utils'.
INFO:absl:Full user module path is 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_utils.py' (including modules: ['penguin_utils']).
INFO:absl:User module package has hash fingerprint version a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpx36cu7js/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp5llre0fh', '--dist-dir', '/tmp/tmp_bp6d6sq']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_utils.py -> build/lib
installing to /tmp/tmpibb6gqep
running install
running install_lib
copying build/lib/penguin_utils.py -> /tmp/tmpibb6gqep
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmpibb6gqep/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3.7.egg-info
running install_scripts
creating /tmp/tmpibb6gqep/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL
creating '/tmp/tmpm0e9w5ft/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' and adding '/tmp/tmpibb6gqep' to it
adding 'penguin_utils.py'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/RECORD'
removing /tmp/tmpibb6gqep
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'; target user module is 'penguin_utils'.
INFO:absl:Full user module path is 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'
INFO:absl:Using deployment config:
 executor_specs {
  key: "CsvExampleGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "ExampleValidator"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_validator.executor.Executor"
    }
  }
}
executor_specs {
  key: "Pusher"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.pusher.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "Trainer"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.trainer.executor.GenericExecutor"
    }
  }
}
executor_specs {
  key: "Transform"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.transform.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  sqlite {
    filename_uri: "metadata/penguin-transform/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "metadata/penguin-transform/metadata.db"
  connection_mode: READWRITE_OPENCREATE
}

INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-11-09T10:58:57.425917"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-data22duh_39"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
  parameters {
    key: "output_file_format"
    value {
      field_value {
        int_value: 5
      }
    }
  }
}
downstream_nodes: "StatisticsGen"
downstream_nodes: "Trainer"
downstream_nodes: "Transform"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1109 10:58:57.456017 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1109 10:58:57.464406 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1109 10:58:57.472069 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1109 10:58:57.481070 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
I1109 10:58:57.500597 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1636455535,sum_checksum:1636455535"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}), exec_properties={'output_data_format': 6, 'output_config': '{\n  "split_config": {\n    "splits": [\n      {\n        "hash_buckets": 2,\n        "name": "train"\n      },\n      {\n        "hash_buckets": 1,\n        "name": "eval"\n      }\n    ]\n  }\n}', 'input_base': '/tmp/tfx-data22duh_39', 'output_file_format': 5, 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:13161,xor_checksum:1636455535,sum_checksum:1636455535'}, execution_output_uri='pipelines/penguin-transform/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/CsvExampleGen/.system/stateful_working_dir/2021-11-09T10:58:57.425917', tmp_dir='pipelines/penguin-transform/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-11-09T10:58:57.425917"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-data22duh_39"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
  parameters {
    key: "output_file_format"
    value {
      field_value {
        int_value: 5
      }
    }
  }
}
downstream_nodes: "StatisticsGen"
downstream_nodes: "Trainer"
downstream_nodes: "Transform"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-11-09T10:58:57.425917')
INFO:absl:Generating examples.
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_utils.py -> build/lib
installing to /tmp/tmp5llre0fh
running install
running install_lib
copying build/lib/penguin_utils.py -> /tmp/tmp5llre0fh
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmp5llre0fh/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3.7.egg-info
running install_scripts
creating /tmp/tmp5llre0fh/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL
creating '/tmp/tmp_bp6d6sq/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' and adding '/tmp/tmp5llre0fh' to it
adding 'penguin_utils.py'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/RECORD'
removing /tmp/tmp5llre0fh
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-data22duh_39/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
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:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1636455535,sum_checksum:1636455535"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.3.3"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component schema_importer is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.dsl.components.common.importer.Importer"
  }
  id: "schema_importer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-11-09T10:58:57.425917"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.schema_importer"
      }
    }
  }
}
outputs {
  outputs {
    key: "result"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "artifact_uri"
    value {
      field_value {
        string_value: "schema"
      }
    }
  }
  parameters {
    key: "reimport"
    value {
      field_value {
        int_value: 0
      }
    }
  }
}
downstream_nodes: "ExampleValidator"
downstream_nodes: "Transform"
execution_options {
  caching_options {
  }
}

INFO:absl:Running as an importer node.
INFO:absl:MetadataStore with DB connection initialized
I1109 10:58:58.770336 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Processing source uri: schema, properties: {}, custom_properties: {}
INFO:absl:Component schema_importer is finished.
I1109 10:58:58.781059 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-11-09T10:58:57.425917"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-transform"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-11-09T10:58:57.425917"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1109 10:58:58.803603 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1636455535,sum_checksum:1636455535"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.3.3"
  }
}
state: LIVE
create_time_since_epoch: 1636455538753
last_update_time_since_epoch: 1636455538753
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-transform/StatisticsGen/statistics/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-transform/StatisticsGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/StatisticsGen/.system/stateful_working_dir/2021-11-09T10:58:57.425917', tmp_dir='pipelines/penguin-transform/StatisticsGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-11-09T10:58:57.425917"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-transform"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-11-09T10:58:57.425917"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-11-09T10:58:57.425917')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to pipelines/penguin-transform/StatisticsGen/statistics/3/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to pipelines/penguin-transform/StatisticsGen/statistics/3/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-transform/StatisticsGen/statistics/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.3.3"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component Transform is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.transform.component.Transform"
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contexts {
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inputs {
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        output_key: "result"
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      min_count: 1
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outputs {
  outputs {
    key: "post_transform_anomalies"
    value {
      artifact_spec {
        type {
          name: "ExampleAnomalies"
          properties {
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          }
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          name: "Schema"
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    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
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        }
      }
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  outputs {
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          name: "TransformGraph"
        }
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    value {
      artifact_spec {
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      }
    }
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parameters {
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      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
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}

INFO:absl:MetadataStore with DB connection initialized
I1109 10:59:01.411818 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 4
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
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    string_value: "1.3.3"
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state: LIVE
create_time_since_epoch: 1636455538785
last_update_time_since_epoch: 1636455538785
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type_id: 15
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properties {
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custom_properties {
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custom_properties {
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custom_properties {
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  value {
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custom_properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1636455538753
last_update_time_since_epoch: 1636455538753
, artifact_type: id: 15
name: "Examples"
properties {
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properties {
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properties {
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)]}, output_dict=defaultdict(<class 'list'>, {'updated_analyzer_cache': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/updated_analyzer_cache/4"
custom_properties {
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custom_properties {
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custom_properties {
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custom_properties {
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, artifact_type: name: "ExampleStatistics"
properties {
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properties {
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custom_properties {
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, artifact_type: name: "Schema"
)]}), exec_properties={'custom_config': 'null', 'disable_statistics': 0, 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'force_tf_compat_v1': 0}, execution_output_uri='pipelines/penguin-transform/Transform/.system/executor_execution/4/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/Transform/.system/stateful_working_dir/2021-11-09T10:58:57.425917', tmp_dir='pipelines/penguin-transform/Transform/.system/executor_execution/4/.temp/', pipeline_node=node_info {
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contexts {
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  contexts {
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inputs {
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    value {
      channels {
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        context_queries {
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        context_queries {
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          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
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        }
        artifact_query {
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        output_key: "examples"
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      min_count: 1
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  inputs {
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    value {
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        artifact_query {
          type {
            name: "Schema"
          }
        }
        output_key: "result"
      }
      min_count: 1
    }
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}
outputs {
  outputs {
    key: "post_transform_anomalies"
    value {
      artifact_spec {
        type {
          name: "ExampleAnomalies"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
  outputs {
    key: "post_transform_schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
  outputs {
    key: "post_transform_stats"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
  outputs {
    key: "pre_transform_schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
  outputs {
    key: "pre_transform_stats"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
  outputs {
    key: "transform_graph"
    value {
      artifact_spec {
        type {
          name: "TransformGraph"
        }
      }
    }
  }
  outputs {
    key: "updated_analyzer_cache"
    value {
      artifact_spec {
        type {
          name: "TransformCache"
        }
      }
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}
parameters {
  parameters {
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    value {
      field_value {
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    value {
      field_value {
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  parameters {
    key: "force_tf_compat_v1"
    value {
      field_value {
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      }
    }
  }
  parameters {
    key: "module_path"
    value {
      field_value {
        string_value: "penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl"
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}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-11-09T10:58:57.425917')
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp6qay5hvx', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpndq5x07w', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: 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 body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpv9y5m0zi', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
2021-11-09 10:59:12.602918: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/8dcdc91e01414660a0ee9dd240230c35/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/8dcdc91e01414660a0ee9dd240230c35/assets
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/02c21e0e828f4474bb98fa4836f2702c/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/02c21e0e828f4474bb98fa4836f2702c/assets
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 4 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'updated_analyzer_cache': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/updated_analyzer_cache/4"
custom_properties {
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custom_properties {
  key: "tfx_version"
  value {
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, artifact_type: name: "Schema"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
I1109 10:59:18.149689 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1109 10:59:18.158646 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Transform is finished.
INFO:absl:Component ExampleValidator is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_validator.component.ExampleValidator"
  }
  id: "ExampleValidator"
}
contexts {
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  contexts {
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    name {
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        string_value: "2021-11-09T10:58:57.425917"
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  contexts {
    type {
      name: "node"
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    name {
      field_value {
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inputs {
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        artifact_query {
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outputs {
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    value {
      artifact_spec {
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execution_options {
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INFO:absl:MetadataStore with DB connection initialized
I1109 10:59:18.185603 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 5
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'statistics': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-transform/StatisticsGen/statistics/3"
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state: LIVE
create_time_since_epoch: 1636455541391
last_update_time_since_epoch: 1636455541391
, artifact_type: id: 19
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)], 'schema': [Artifact(artifact: id: 2
type_id: 17
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, artifact_type: id: 17
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    value {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-11-09T10:58:57.425917')
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to pipelines/penguin-transform/ExampleValidator/anomalies/5/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to pipelines/penguin-transform/ExampleValidator/anomalies/5/Split-eval.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 5 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-transform/ExampleValidator/anomalies/5"
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, artifact_type: name: "ExampleAnomalies"
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properties {
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component ExampleValidator is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
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  id: "Trainer"
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contexts {
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  contexts {
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inputs {
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outputs {
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parameters {
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INFO:absl:MetadataStore with DB connection initialized
I1109 10:59:18.243136 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 6
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'transform_graph': [Artifact(artifact: id: 8
type_id: 23
uri: "pipelines/penguin-transform/Transform/transform_graph/4"
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custom_properties {
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upstream_nodes: "CsvExampleGen"
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execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-11-09T10:58:57.425917')
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.
INFO:absl:udf_utils.get_fn {'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'custom_config': 'null', 'train_args': '{\n  "num_steps": 100\n}', 'eval_args': '{\n  "num_steps": 5\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpg9f1r2nt', 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:culmen_length_mm (InputLayer)   [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer)    [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer)  [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:body_mass_g (InputLayer)        [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 4)            0           culmen_length_mm[0][0]           
INFO:absl:                                                                 culmen_depth_mm[0][0]            
INFO:absl:                                                                 flipper_length_mm[0][0]          
INFO:absl:                                                                 body_mass_g[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 8)            40          concatenate[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 3)            27          dense_1[0][0]                    
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 1s 4ms/step - loss: 0.3091 - sparse_categorical_accuracy: 0.9090 - val_loss: 0.0165 - val_sparse_categorical_accuracy: 1.0000
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Trainer/model/6/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-transform/Trainer/model/6/Format-Serving. ModelRun written to pipelines/penguin-transform/Trainer/model_run/6
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 6 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model/6"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:Trainer:model:0"
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custom_properties {
  key: "tfx_version"
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    string_value: "1.3.3"
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, artifact_type: name: "Model"
)], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model_run/6"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-11-09T10:58:57.425917:Trainer:model_run:0"
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custom_properties {
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, artifact_type: name: "ModelRun"
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Trainer is finished.
I1109 10:59:25.219398 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is running.
I1109 10:59:25.223850 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.pusher.component.Pusher"
  }
  id: "Pusher"
}
contexts {
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        artifact_query {
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execution_options {
  caching_options {
  }
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INFO:absl:MetadataStore with DB connection initialized
I1109 10:59:25.248777 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 7
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=7, input_dict={'model': [Artifact(artifact: id: 12
type_id: 26
uri: "pipelines/penguin-transform/Trainer/model/6"
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custom_properties {
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state: LIVE
create_time_since_epoch: 1636455565228
last_update_time_since_epoch: 1636455565228
, artifact_type: id: 26
name: "Model"
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custom_properties {
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, artifact_type: name: "PushedModel"
)]}), exec_properties={'custom_config': 'null', 'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-transform"\n  }\n}'}, execution_output_uri='pipelines/penguin-transform/Pusher/.system/executor_execution/7/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/Pusher/.system/stateful_working_dir/2021-11-09T10:58:57.425917', tmp_dir='pipelines/penguin-transform/Pusher/.system/executor_execution/7/.temp/', pipeline_node=node_info {
  type {
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  id: "Pusher"
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contexts {
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  contexts {
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inputs {
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        artifact_query {
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        output_key: "model"
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outputs {
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    value {
      artifact_spec {
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execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-11-09T10:58:57.425917')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1636455565
INFO:absl:Model written to serving path serving_model/penguin-transform/1636455565.
INFO:absl:Model pushed to pipelines/penguin-transform/Pusher/pushed_model/7.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 7 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-transform/Pusher/pushed_model/7"
custom_properties {
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custom_properties {
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, artifact_type: name: "PushedModel"
)]}) for execution 7
INFO:absl:MetadataStore with DB connection initialized
I1109 10:59:25.284077 31805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is finished.

Debería ver "INFO: absl: Component Pusher está terminado". si la canalización terminó con éxito.

El componente empujador empuja el modelo entrenado para la SERVING_MODEL_DIR que es el serving_model/penguin-transform directorio si no ha cambiado las variables en los pasos anteriores. Puede ver el resultado desde el navegador de archivos en el panel del lado izquierdo en Colab, o usando el siguiente comando:

# List files in created model directory.
find {SERVING_MODEL_DIR}
serving_model/penguin-transform
serving_model/penguin-transform/1636455565
serving_model/penguin-transform/1636455565/saved_model.pb
serving_model/penguin-transform/1636455565/variables
serving_model/penguin-transform/1636455565/variables/variables.data-00000-of-00001
serving_model/penguin-transform/1636455565/variables/variables.index
serving_model/penguin-transform/1636455565/keras_metadata.pb
serving_model/penguin-transform/1636455565/assets

También puede comprobar la firma del modelo generado mediante el saved_model_cli herramienta .

saved_model_cli show --dir {SERVING_MODEL_DIR}/$(ls -1 {SERVING_MODEL_DIR} | sort -nr | head -1) --tag_set serve --signature_def serving_default
The given SavedModel SignatureDef contains the following input(s):
  inputs['examples'] tensor_info:
      dtype: DT_STRING
      shape: (-1)
      name: serving_default_examples:0
The given SavedModel SignatureDef contains the following output(s):
  outputs['output_0'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, 3)
      name: StatefulPartitionedCall_2:0
Method name is: tensorflow/serving/predict

Debido a que hemos definido serving_default con nuestra propia serve_tf_examples_fn función, los espectáculos de la firma que se necesita una sola cadena. Esta cadena es una cadena serializada de tf.Examples y será analizado con el tf.io.parse_example () función que hemos definido anteriormente (más información acerca tf.Examples aquí ).

Podemos cargar el modelo exportado y probar algunas inferencias con algunos ejemplos.

# Find a model with the latest timestamp.
model_dirs = (item for item in os.scandir(SERVING_MODEL_DIR) if item.is_dir())
model_path = max(model_dirs, key=lambda i: int(i.name)).path

loaded_model = tf.keras.models.load_model(model_path)
inference_fn = loaded_model.signatures['serving_default']
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fd4aba6b5d0> and <keras.engine.input_layer.InputLayer object at 0x7fd37c391550>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7fd4aba6b5d0> and <keras.engine.input_layer.InputLayer object at 0x7fd37c391550>).
# Prepare an example and run inference.
features = {
  'culmen_length_mm': tf.train.Feature(float_list=tf.train.FloatList(value=[49.9])),
  'culmen_depth_mm': tf.train.Feature(float_list=tf.train.FloatList(value=[16.1])),
  'flipper_length_mm': tf.train.Feature(int64_list=tf.train.Int64List(value=[213])),
  'body_mass_g': tf.train.Feature(int64_list=tf.train.Int64List(value=[5400])),
}
example_proto = tf.train.Example(features=tf.train.Features(feature=features))
examples = example_proto.SerializeToString()

result = inference_fn(examples=tf.constant([examples]))
print(result['output_0'].numpy())
[[-4.4002094 -6.018669   3.6988904]]

Se espera que el tercer elemento, que corresponde a la especie 'Gentoo', sea el más grande entre tres.

Próximos pasos

Si desea obtener más información sobre Transformar componente, consulte Transformar guía de componentes . Puede encontrar más recursos en https://www.tensorflow.org/tfx/tutorials

Por favor, vea Comprender TFX Tuberías para aprender más acerca de los diversos conceptos en TFX.