Una introducción componente por componente a TensorFlow Extended (TFX)
Este tutorial basado en Colab recorrerá de forma interactiva cada componente integrado de TensorFlow Extended (TFX).
Cubre cada paso en una canalización de aprendizaje automático de un extremo a otro, desde la ingestión de datos hasta la implementación de un modelo y la entrega.
Cuando haya terminado, el contenido de este cuaderno se puede exportar automáticamente como código fuente de canalización TFX, que puede organizar con Apache Airflow y Apache Beam.
Fondo
Este cuaderno demuestra cómo utilizar TFX en un entorno Jupyter / Colab. Aquí, recorremos el ejemplo de Chicago Taxi en un cuaderno interactivo.
Trabajar en un cuaderno interactivo es una forma útil de familiarizarse con la estructura de una canalización TFX. También es útil cuando se realiza el desarrollo de sus propias canalizaciones como un entorno de desarrollo ligero, pero debe tener en cuenta que existen diferencias en la forma en que se organizan los cuadernos interactivos y cómo acceden a los artefactos de metadatos.
Orquestación
En una implementación de producción de TFX, utilizará un orquestador como Apache Airflow, Kubeflow Pipelines o Apache Beam para orquestar un gráfico de canalización predefinido de componentes TFX. En un cuaderno interactivo, el propio cuaderno es el orquestador y ejecuta cada componente TFX a medida que ejecuta las celdas del cuaderno.
Metadatos
En una implementación de producción de TFX, accederá a los metadatos a través de la API de metadatos ML (MLMD). MLMD almacena las propiedades de los metadatos en una base de datos como MySQL o SQLite, y almacena las cargas útiles de los metadatos en un almacén persistente como en su sistema de archivos. En un cuaderno interactivo, ambas propiedades y cargas útiles se almacenan en una base de datos SQLite efímera en el /tmp
directorio en el ordenador portátil o servidor Jupyter Colab.
Configuración
Primero, instalamos e importamos los paquetes necesarios, configuramos rutas y descargamos datos.
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
¿Reiniciaste el tiempo de ejecución?
Si está utilizando Google Colab, la primera vez que ejecuta la celda anterior, debe reiniciar el tiempo de ejecución (Tiempo de ejecución> Reiniciar tiempo de ejecución ...). Esto se debe a la forma en que Colab carga los paquetes.
Importar paquetes
Importamos los paquetes necesarios, incluidas las clases de componentes TFX estándar.
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()
from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip
Revisemos las versiones de la biblioteca.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.7.0 TFX version: 1.5.0
Configurar rutas de canalización
# 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)
Descargar datos de ejemplo
Descargamos el conjunto de datos de ejemplo para usarlo en nuestra canalización TFX.
El conjunto de datos que estamos utilizando es el Taxi Viajes conjunto de datos dado a conocer por la ciudad de Chicago. Las columnas de este conjunto de datos son:
pickup_community_area | tarifa | 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 | tipo de pago | empresa |
trip_seconds | dropoff_community_area | consejos |
Con este conjunto de datos, vamos a construir un modelo que predice los tips
de un viaje.
_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-datacz9xjro6/data.csv', <http.client.HTTPMessage at 0x7f889af49250>)
Eche un vistazo rápido al archivo CSV.
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
Descargo de responsabilidad: este sitio proporciona aplicaciones que utilizan datos que se han modificado para su uso desde su fuente original, www.cityofchicago.org, el sitio web oficial de la ciudad de Chicago. La ciudad de Chicago no se responsabiliza del contenido, precisión, puntualidad o integridad de los datos proporcionados en este sitio. Los datos proporcionados en este sitio están sujetos a cambios en cualquier momento. Se entiende que los datos proporcionados en este sitio se utilizan bajo su propia responsabilidad.
Crear el contexto interactivo
Por último, creamos un InteractiveContext, que nos permitirá ejecutar componentes TFX de forma interactiva en este cuaderno.
# 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-2021-12-21T10_09_51.902969-bvucg0eq as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/metadata.sqlite.
Ejecute componentes TFX de forma interactiva
En las celdas que siguen, creamos componentes TFX uno por uno, ejecutamos cada uno de ellos y visualizamos sus artefactos de salida.
ExampleGen
El ExampleGen
componente es por lo general en el inicio de una tubería TFX. Va a:
- Divida los datos en conjuntos de entrenamiento y evaluación (de forma predeterminada, 2/3 de entrenamiento + 1/3 de evaluación)
- Los datos se convierten en el
tf.Example
formato (aprender más aquí ) - Copiar datos en el
_tfx_root
directorio para el acceso a otros componentes
ExampleGen
toma como entrada el camino a la fuente de datos. En nuestro caso, este es el _data_root
ruta que contiene el archivo CSV descargado.
example_gen = tfx.components.CsvExampleGen(input_base=_data_root)
context.run(example_gen)
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-datacz9xjro6/* 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:Running publisher for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized
Vamos a examinar los artefactos de salida de ExampleGen
. Este componente produce dos artefactos, ejemplos de formación y ejemplos de evaluación:
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/CsvExampleGen/examples/1
También podemos echar un vistazo a los tres primeros ejemplos de formación:
# 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 } } } }
Ahora que ExampleGen
ha terminado la ingestión de los datos, el siguiente paso es el análisis de datos.
EstadísticaGen
Los StatisticsGen
Calcula los componentes estadísticas sobre el conjunto de datos para el análisis de datos, así como para su uso en componentes de nivel inferior. Utiliza el TensorFlow validación de datos de la biblioteca.
StatisticsGen
toma como entrada el conjunto de datos que acabamos ingerido usando ExampleGen
.
statistics_gen = tfx.components.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-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-train. INFO:absl:Generating statistics for split eval. INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-eval. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:absl:Running publisher for StatisticsGen INFO:absl:MetadataStore with DB connection initialized
Después StatisticsGen
termina de ejecutarse, podemos visualizar las estadísticas emitidas. ¡Intenta jugar con las diferentes tramas!
context.show(statistics_gen.outputs['statistics'])
SchemaGen
El SchemaGen
componente genera un esquema basado en las estadísticas de datos. (Un esquema define los límites de lo esperado, tipos y propiedades de las características en el conjunto de datos.) También se utiliza el TensorFlow validación de datos de la biblioteca.
SchemaGen
tomará como entrada las estadísticas que hemos generado con StatisticsGen
, mirando a la división de entrenamiento por defecto.
schema_gen = tfx.components.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-2021-12-21T10_09_51.902969-bvucg0eq/SchemaGen/schema/3/schema.pbtxt. INFO:absl:Running publisher for SchemaGen INFO:absl:MetadataStore with DB connection initialized
Después SchemaGen
finaliza su ejecución, podemos visualizar el esquema generado como una tabla.
context.show(schema_gen.outputs['schema'])
Cada característica en su conjunto de datos se muestra como una fila en la tabla de esquema, junto con sus propiedades. El esquema también captura todos los valores que asume una característica categórica, denotada como su dominio.
Para obtener más información acerca de los esquemas, vea la documentación SchemaGen .
ExampleValidator
El ExampleValidator
componente detecta anomalías en los datos, en función de las expectativas definidas por el esquema. También se utiliza el TensorFlow validación de datos de la biblioteca.
ExampleValidator
tomará como entrada las estadísticas de StatisticsGen
, y el esquema de SchemaGen
.
example_validator = tfx.components.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-2021-12-21T10_09_51.902969-bvucg0eq/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-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-eval. INFO:absl:Running publisher for ExampleValidator INFO:absl:MetadataStore with DB connection initialized
Después ExampleValidator
finaliza su ejecución, podemos visualizar las anomalías como una mesa.
context.show(example_validator.outputs['anomalies'])
En la tabla de anomalías podemos ver que no hay anomalías. Esto es lo que esperaríamos, ya que este es el primer conjunto de datos que analizamos y el esquema se adapta a él. Debe revisar este esquema: cualquier cosa inesperada significa una anomalía en los datos. Una vez revisado, el esquema se puede usar para proteger datos futuros, y las anomalías producidas aquí se pueden usar para depurar el rendimiento del modelo, comprender cómo evolucionan sus datos con el tiempo e identificar errores de datos.
Transformar
El Transform
realice el componente de ingeniería característica tanto para la formación y servir. Utiliza el TensorFlow Transform biblioteca.
Transform
tomará como entrada los datos de ExampleGen
, el esquema de SchemaGen
, así como un módulo que contiene transformar código definido por el usuario.
Veamos un ejemplo de usuario definido Transformar código de abajo (para una introducción a la TensorFlow Transformar APIs, ver el tutorial ). Primero, definimos algunas constantes para la ingeniería de características:
_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'
Writing taxi_constants.py
A continuación, escribir preprocessing_fn
que lleva en datos en bruto como entrada, y vuelve características transformadas que nuestro modelo puede entrenar en:
_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
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:
# If sparse make it dense, setting nan's to 0 or '', and apply zscore.
outputs[key] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[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[key] = tft.bucketize(
_fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[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[_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
Ahora, pasamos en este código de ingeniería de función para la Transform
de componentes y ejecutarlo para transformar los datos.
transform = tfx.components.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:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_transform', 'taxi_constants']). INFO:absl:User module package has hash fingerprint version f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp9qnpryw9/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmppaskl3va', '--dist-dir', '/tmp/tmpr6oorqji'] /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 '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'; target user module is 'taxi_transform'. INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' 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. INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbvbj9r5b', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] running bdist_wheel running build running build_py creating build creating build/lib copying taxi_transform.py -> build/lib copying taxi_constants.py -> build/lib running install running install_lib running install_egg_info running egg_info creating tfx_user_code_Transform.egg-info writing 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/tmppaskl3va/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3.7.egg-info running install_scripts Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbzwdie1a', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp09euava5', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:Feature company 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 payment_type 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. Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use ref() instead. INFO:absl:Feature company 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 payment_type 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:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:Feature company 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 payment_type 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 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 payment_type 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 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 payment_type 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 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 payment_type 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], int] instead. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. 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`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2 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`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2 INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. 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`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2 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`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2 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. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:Feature company 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 payment_type 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 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 payment_type 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:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. 2021-12-21 10:10:18.679569: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/80dbc09e6ded4a93b5c506e252c8f536/assets INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/572eacb7c64f4f6e9262f7d496a95f86/assets INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:absl:Running publisher for Transform INFO:absl:MetadataStore with DB connection initialized
Vamos a examinar los artefactos de salida de Transform
. Este componente produce dos tipos de salidas:
-
transform_graph
es el gráfico que pueden realizar las operaciones de preprocesamiento (este gráfico se incluirá en los modelos de la porción y de evaluación). -
transformed_examples
representa los datos de entrenamiento y evaluación preprocesados.
transform.outputs
{'transform_graph': Channel( type_name: TransformGraph artifacts: [Artifact(artifact: id: 5 type_id: 22 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/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: "1.5.0" } } state: LIVE , artifact_type: id: 22 name: "TransformGraph" )] additional_properties: {} additional_custom_properties: {} ), 'transformed_examples': Channel( type_name: Examples artifacts: [Artifact(artifact: id: 6 type_id: 14 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/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: "1.5.0" } } state: LIVE , artifact_type: id: 14 name: "Examples" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } properties { key: "version" value: INT } base_type: DATASET )] additional_properties: {} additional_custom_properties: {} ), 'updated_analyzer_cache': Channel( type_name: TransformCache artifacts: [Artifact(artifact: id: 7 type_id: 23 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/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: "1.5.0" } } state: LIVE , artifact_type: id: 23 name: "TransformCache" )] additional_properties: {} additional_custom_properties: {} ), 'pre_transform_schema': Channel( type_name: Schema artifacts: [Artifact(artifact: id: 8 type_id: 18 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_schema/5" custom_properties { key: "name" value { string_value: "pre_transform_schema" } } 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: "1.5.0" } } state: LIVE , artifact_type: id: 18 name: "Schema" )] additional_properties: {} additional_custom_properties: {} ), 'pre_transform_stats': Channel( type_name: ExampleStatistics artifacts: [Artifact(artifact: id: 9 type_id: 16 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_stats/5" custom_properties { key: "name" value { string_value: "pre_transform_stats" } } 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: "1.5.0" } } state: LIVE , artifact_type: id: 16 name: "ExampleStatistics" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } base_type: STATISTICS )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_schema': Channel( type_name: Schema artifacts: [Artifact(artifact: id: 10 type_id: 18 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_schema/5" custom_properties { key: "name" value { string_value: "post_transform_schema" } } 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: "1.5.0" } } state: LIVE , artifact_type: id: 18 name: "Schema" )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_stats': Channel( type_name: ExampleStatistics artifacts: [Artifact(artifact: id: 11 type_id: 16 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_stats/5" custom_properties { key: "name" value { string_value: "post_transform_stats" } } 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: "1.5.0" } } state: LIVE , artifact_type: id: 16 name: "ExampleStatistics" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } base_type: STATISTICS )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_anomalies': Channel( type_name: ExampleAnomalies artifacts: [Artifact(artifact: id: 12 type_id: 20 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_anomalies/5" custom_properties { key: "name" value { string_value: "post_transform_anomalies" } } 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: "1.5.0" } } state: LIVE , artifact_type: id: 20 name: "ExampleAnomalies" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } )] additional_properties: {} additional_custom_properties: {} )}
Echar un vistazo a la transform_graph
artefacto. Apunta a un directorio que contiene tres subdirectorios.
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
El transformed_metadata
subdirectorio contiene el esquema de los datos que se procesan. El transform_fn
subdirectorio contiene el gráfico de preprocesamiento real. El metadata
subdirectorio contiene el esquema de los datos originales.
También podemos echar un vistazo a los primeros tres ejemplos transformados:
# 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" value { int64_list { value: 8 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 0.061060599982738495 } } } feature { key: "payment_type" value { int64_list { value: 1 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 0 } } } feature { key: "pickup_latitude" value { int64_list { value: 0 } } } feature { key: "pickup_longitude" value { int64_list { value: 9 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: -0.15886741876602173 } } } feature { key: "trip_seconds" value { float_list { value: -0.7118487358093262 } } } 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 } } } } features { feature { key: "company" value { int64_list { value: 0 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 1.2521240711212158 } } } feature { key: "payment_type" value { int64_list { value: 0 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 60 } } } feature { key: "pickup_latitude" value { int64_list { value: 0 } } } feature { key: "pickup_longitude" value { int64_list { value: 3 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: 0.532160758972168 } } } feature { key: "trip_seconds" value { float_list { value: 0.5509493350982666 } } } 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 } } } } features { feature { key: "company" value { int64_list { value: 48 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 0.3873794376850128 } } } feature { key: "payment_type" value { int64_list { value: 0 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 13 } } } feature { key: "pickup_latitude" value { int64_list { value: 9 } } } feature { key: "pickup_longitude" value { int64_list { value: 0 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: 0.21955277025699615 } } } feature { key: "trip_seconds" value { float_list { value: 0.0019067146349698305 } } } 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 } } } }
Después de la Transform
componente ha transformado sus datos en características, y el siguiente paso es la formación de un modelo.
Entrenador
El Trainer
componente formará a un modelo que defina en TensorFlow. Por defecto apoyo Trainer Estimador de API, para utilizar la API Keras, es necesario especificar Trainer genérico de configuración custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor)
en contructor del capacitador.
Trainer
toma como entrada el esquema de SchemaGen
, los datos transformados y el gráfico de Transform
, la formación de parámetros, así como un módulo que contiene el código del modelo definido por el usuario.
Veamos un ejemplo de código de modelo definido por el usuario a continuación (para una introducción a las TensorFlow Keras APIs, ver el tutorial ):
_taxi_trainer_module_file = 'taxi_trainer.py'
%%writefile {_taxi_trainer_module_file}
from typing import List, Text
import os
from absl import logging
import datetime
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
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
def _get_tf_examples_serving_signature(model, tf_transform_output):
"""Returns a serving signature that accepts `tensorflow.Example`."""
# We need to track the layers in the model in order to save it.
# TODO(b/162357359): Revise once the bug is resolved.
model.tft_layer_inference = 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_example):
"""Returns the output to be used in the serving signature."""
raw_feature_spec = tf_transform_output.raw_feature_spec()
# Remove label feature since these will not be present at serving time.
raw_feature_spec.pop(_LABEL_KEY)
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer_inference(raw_features)
logging.info('serve_transformed_features = %s', transformed_features)
outputs = model(transformed_features)
# TODO(b/154085620): Convert the predicted labels from the model using a
# reverse-lookup (opposite of transform.py).
return {'outputs': outputs}
return serve_tf_examples_fn
def _get_transform_features_signature(model, tf_transform_output):
"""Returns a serving signature that applies tf.Transform to features."""
# We need to track the layers in the model in order to save it.
# TODO(b/162357359): Revise once the bug is resolved.
model.tft_layer_eval = tf_transform_output.transform_features_layer()
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
])
def transform_features_fn(serialized_tf_example):
"""Returns the transformed_features to be fed as input to evaluator."""
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer_eval(raw_features)
logging.info('eval_transformed_features = %s', transformed_features)
return transformed_features
return transform_features_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.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_LABEL_KEY),
tf_transform_output.transformed_metadata.schema)
def _build_keras_model(hidden_units: List[int] = None) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying taxi data.
Args:
hidden_units: [int], the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _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 _VOCAB_FEATURE_KEYS
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
for key in _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(
_CATEGORICAL_FEATURE_KEYS,
_MAX_CATEGORICAL_FEATURE_VALUES)
]
indicator_column = [
tf.feature_column.indicator_column(categorical_column)
for categorical_column in categorical_columns
]
model = _wide_and_deep_classifier(
# TODO(b/139668410) replace with premade wide_and_deep keras model
wide_columns=indicator_column,
deep_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25])
return model
def _wide_and_deep_classifier(wide_columns, deep_columns, dnn_hidden_units):
"""Build a simple keras wide and deep model.
Args:
wide_columns: Feature columns wrapped in indicator_column for wide (linear)
part of the model.
deep_columns: Feature columns for deep part of the model.
dnn_hidden_units: [int], the layer sizes of the hidden DNN.
Returns:
A Wide and Deep Keras model
"""
# Following values are hard coded for simplicity in this example,
# However prefarably they should be passsed in as hparams.
# Keras needs the feature definitions at compile time.
# TODO(b/139081439): Automate generation of input layers from FeatureColumn.
input_layers = {
colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
for colname in _DENSE_FLOAT_FEATURE_KEYS
}
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _VOCAB_FEATURE_KEYS
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _BUCKET_FEATURE_KEYS
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _CATEGORICAL_FEATURE_KEYS
})
# TODO(b/161952382): Replace with Keras preprocessing layers.
deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
for numnodes in dnn_hidden_units:
deep = tf.keras.layers.Dense(numnodes)(deep)
wide = tf.keras.layers.DenseFeatures(wide_columns)(input_layers)
output = tf.keras.layers.Dense(1)(
tf.keras.layers.concatenate([deep, wide]))
model = tf.keras.Model(input_layers, output)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(lr=0.001),
metrics=[tf.keras.metrics.BinaryAccuracy()])
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.
"""
# Number of nodes in the first layer of the DNN
first_dnn_layer_size = 100
num_dnn_layers = 4
dnn_decay_factor = 0.7
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor,
tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor,
tf_transform_output, 40)
model = _build_keras_model(
# 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)
])
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=fn_args.model_run_dir, update_freq='batch')
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_tf_examples_serving_signature(model, tf_transform_output),
'transform_features':
_get_transform_features_signature(model, tf_transform_output),
}
model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing taxi_trainer.py
Ahora, pasamos en este código modelo al Trainer
componente y ejecutarlo para entrenar el modelo.
trainer = tfx.components.Trainer(
module_file=os.path.abspath(_taxi_trainer_module_file),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=tfx.proto.TrainArgs(num_steps=10000),
eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_transform', 'taxi_constants', 'taxi_trainer']). INFO:absl:User module package has hash fingerprint version ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpzxd5b1yc/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpbg9ly6tr', '--dist-dir', '/tmp/tmpx43qh690'] /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 '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'; target user module is 'taxi_trainer'. INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' 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:udf_utils.get_fn {'train_args': '{\n "num_steps": 10000\n}', 'eval_args': '{\n "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'} 'run_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp1osq6e1x', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'] running bdist_wheel running build running build_py creating build creating build/lib copying taxi_transform.py -> build/lib copying taxi_constants.py -> build/lib copying taxi_trainer.py -> build/lib running install running install_lib running install_egg_info running egg_info creating tfx_user_code_Trainer.egg-info writing 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/tmpbg9ly6tr/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3.7.egg-info running install_scripts Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'. INFO:absl:Training model. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. Installing collected packages: tfx-user-code-Trainer Successfully installed tfx-user-code-Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(Adam, self).__init__(name, **kwargs) INFO:absl:Model: "model" INFO:absl:__________________________________________________________________________________________________ INFO:absl: Layer (type) Output Shape Param # Connected to INFO:absl:================================================================================================== INFO:absl: company (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dropoff_census_tract (InputLay [(None,)] 0 [] INFO:absl: er) INFO:absl: INFO:absl: dropoff_community_area (InputL [(None,)] 0 [] INFO:absl: ayer) INFO:absl: INFO:absl: dropoff_latitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dropoff_longitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: fare (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: payment_type (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: pickup_census_tract (InputLaye [(None,)] 0 [] INFO:absl: r) INFO:absl: INFO:absl: pickup_community_area (InputLa [(None,)] 0 [] INFO:absl: yer) INFO:absl: INFO:absl: pickup_latitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: pickup_longitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_miles (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_seconds (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_day (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_hour (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_month (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dense_features (DenseFeatures) (None, 3) 0 ['company[0][0]', INFO:absl: 'dropoff_census_tract[0][0]', INFO:absl: 'dropoff_community_area[0][0]', INFO:absl: 'dropoff_latitude[0][0]', INFO:absl: 'dropoff_longitude[0][0]', INFO:absl: 'fare[0][0]', INFO:absl: 'payment_type[0][0]', INFO:absl: 'pickup_census_tract[0][0]', INFO:absl: 'pickup_community_area[0][0]', INFO:absl: 'pickup_latitude[0][0]', INFO:absl: 'pickup_longitude[0][0]', INFO:absl: 'trip_miles[0][0]', INFO:absl: 'trip_seconds[0][0]', INFO:absl: 'trip_start_day[0][0]', INFO:absl: 'trip_start_hour[0][0]', INFO:absl: 'trip_start_month[0][0]'] INFO:absl: INFO:absl: dense (Dense) (None, 100) 400 ['dense_features[0][0]'] INFO:absl: INFO:absl: dense_1 (Dense) (None, 70) 7070 ['dense[0][0]'] INFO:absl: INFO:absl: dense_2 (Dense) (None, 48) 3408 ['dense_1[0][0]'] INFO:absl: INFO:absl: dense_3 (Dense) (None, 34) 1666 ['dense_2[0][0]'] INFO:absl: INFO:absl: dense_features_1 (DenseFeature (None, 2127) 0 ['company[0][0]', INFO:absl: s) 'dropoff_census_tract[0][0]', INFO:absl: 'dropoff_community_area[0][0]', INFO:absl: 'dropoff_latitude[0][0]', INFO:absl: 'dropoff_longitude[0][0]', INFO:absl: 'fare[0][0]', INFO:absl: 'payment_type[0][0]', INFO:absl: 'pickup_census_tract[0][0]', INFO:absl: 'pickup_community_area[0][0]', INFO:absl: 'pickup_latitude[0][0]', INFO:absl: 'pickup_longitude[0][0]', INFO:absl: 'trip_miles[0][0]', INFO:absl: 'trip_seconds[0][0]', INFO:absl: 'trip_start_day[0][0]', INFO:absl: 'trip_start_hour[0][0]', INFO:absl: 'trip_start_month[0][0]'] INFO:absl: INFO:absl: concatenate (Concatenate) (None, 2161) 0 ['dense_3[0][0]', INFO:absl: 'dense_features_1[0][0]'] INFO:absl: INFO:absl: dense_4 (Dense) (None, 1) 2162 ['concatenate[0][0]'] INFO:absl: INFO:absl:================================================================================================== INFO:absl:Total params: 14,706 INFO:absl:Trainable params: 14,706 INFO:absl:Non-trainable params: 0 INFO:absl:__________________________________________________________________________________________________ 10000/10000 [==============================] - 100s 10ms/step - loss: 0.2372 - binary_accuracy: 0.8605 - val_loss: 0.2222 - val_binary_accuracy: 0.8709 INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert INFO:absl:serve_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>} INFO:absl:eval_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'tips': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:11' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>} INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving/assets INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model_run/6 INFO:absl:Running publisher for Trainer INFO:absl:MetadataStore with DB connection initialized
Analizar el entrenamiento con TensorBoard
Eche un vistazo al artefacto del entrenador. Apunta a un directorio que contiene los subdirectorios del modelo.
model_artifact_dir = trainer.outputs['model'].get()[0].uri
pp.pprint(os.listdir(model_artifact_dir))
model_dir = os.path.join(model_artifact_dir, 'Format-Serving')
pp.pprint(os.listdir(model_dir))
['Format-Serving'] ['variables', 'assets', 'keras_metadata.pb', 'saved_model.pb']
Opcionalmente, podemos conectar TensorBoard al Trainer para analizar las curvas de entrenamiento de nuestro modelo.
model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}
Evaluador
El Evaluator
componente calcula las métricas de rendimiento modelo sobre el conjunto de la evaluación. Utiliza el TensorFlow Modelo de Análisis biblioteca. El Evaluator
también puede validar opcionalmente que un modelo recién entrenado es mejor que el modelo anterior. Esto es útil en una configuración de canal de producción donde puede entrenar y validar automáticamente un modelo todos los días. En este cuaderno, sólo entrenamos un modelo, por lo que el Evaluator
forma automática etiquetará el modelo como "buena".
Evaluator
tomará como entrada los datos de ExampleGen
, el modelo entrenado del Trainer
, y la configuración de corte. La configuración de división le permite dividir sus métricas en valores de características (por ejemplo, ¿cómo se desempeña su modelo en viajes en taxi que comienzan a las 8 a. M. Frente a las 8 p. M.?). Vea un ejemplo de esta configuración a continuación:
eval_config = tfma.EvalConfig(
model_specs=[
# This assumes a serving model with signature 'serving_default'. If
# using estimator based EvalSavedModel, add signature_name: 'eval' and
# remove the label_key.
tfma.ModelSpec(
signature_name='serving_default',
label_key='tips',
preprocessing_function_names=['transform_features'],
)
],
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.
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
metrics=[
tfma.MetricConfig(class_name='ExampleCount'),
tfma.MetricConfig(class_name='BinaryAccuracy',
threshold=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'])
])
A continuación, damos a esta configuración Evaluator
y ejecutarlo.
# 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 = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
context.run(model_resolver)
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for latest_blessed_model_resolver INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running publisher for 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 INFO:absl:Nonempty beam arg extra_packages already includes dependency INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips",\n "preprocessing_function_names": [\n "transform_features"\n ],\n "signature_name": "serving_default"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model' 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: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } } INFO:absl:Using /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving as model. 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 0x7f87bc0f5e50> and <keras.engine.input_layer.InputLayer object at 0x7f87bc0f5b50>). INFO:absl:The 'example_splits' parameter is not set, using 'eval' split. INFO:absl:Evaluating model. INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips",\n "preprocessing_function_names": [\n "transform_features"\n ],\n "signature_name": "serving_default"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors' 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: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } 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: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } 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: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } 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 0x7f87b0102150> and <keras.engine.input_layer.InputLayer object at 0x7f875454e810>). WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. 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 0x7f87b06c9d50> and <keras.engine.input_layer.InputLayer object at 0x7f87d4041290>). 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 0x7f874c8d6a10> and <keras.engine.input_layer.InputLayer object at 0x7f874c8ac0d0>). 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 0x7f830dcf9fd0> and <keras.engine.input_layer.InputLayer object at 0x7f830dd87110>). 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 0x7f830dc8cad0> and <keras.engine.input_layer.InputLayer object at 0x7f830cf892d0>). 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 0x7f87b041add0> and <keras.engine.input_layer.InputLayer object at 0x7f874d6b6d50>). 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 0x7f830c42a5d0> and <keras.engine.input_layer.InputLayer object at 0x7f830c3037d0>). INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8. INFO:absl:Checking validation results. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: 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-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8. INFO:absl:Running publisher for Evaluator INFO:absl:MetadataStore with DB connection initialized
Ahora vamos a examinar los artefactos de salida del Evaluator
.
evaluator.outputs
{'evaluation': Channel( type_name: ModelEvaluation artifacts: [Artifact(artifact: id: 15 type_id: 29 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/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: "1.5.0" } } state: LIVE , artifact_type: id: 29 name: "ModelEvaluation" )] additional_properties: {} additional_custom_properties: {} ), 'blessing': Channel( type_name: ModelBlessing artifacts: [Artifact(artifact: id: 16 type_id: 30 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8" custom_properties { key: "blessed" value { int_value: 1 } } custom_properties { key: "current_model" value { string_value: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6" } } custom_properties { key: "current_model_id" value { int_value: 13 } } 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: "1.5.0" } } state: LIVE , artifact_type: id: 30 name: "ModelBlessing" )] additional_properties: {} additional_custom_properties: {} )}
El uso de la evaluation
de salida que puede mostrar la visualización de mediciones predeterminadas globales en todo el conjunto de la evaluación.
context.show(evaluator.outputs['evaluation'])
Para ver la visualización de las métricas de evaluación divididas, podemos llamar directamente a la biblioteca de análisis de modelos de TensorFlow.
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',…
Esta visualización muestra los mismos parámetros, pero calcula en cada valor de característica del trip_start_hour
en lugar de en todo el conjunto de la evaluación.
El análisis del modelo de TensorFlow admite muchas otras visualizaciones, como los indicadores de equidad y el trazado de una serie temporal del rendimiento del modelo. Para obtener más información, consulte el tutorial .
Dado que agregamos umbrales a nuestra configuración, la salida de validación también está disponible. El precence de una blessing
artefacto indica que nuestro modelo pasa la validación. Dado que esta es la primera validación que se realiza, el candidato es automáticamente bendecido.
blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0 -rw-rw-r-- 1 kbuilder kbuilder 0 Dec 21 10:13 BLESSED
Ahora también puede verificar el éxito cargando el registro de resultados de validación:
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 } }
Arribista
El Pusher
componente es por lo general al final de una tubería TFX. Comprueba si un modelo ha pasado la validación, y si es así, el modelo de las exportaciones a _serving_model_dir
.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.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: 1640081600 INFO:absl:Model written to serving path /tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600. INFO:absl:Model pushed to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9. INFO:absl:Running publisher for Pusher INFO:absl:MetadataStore with DB connection initialized
Vamos a examinar los artefactos de salida del Pusher
.
pusher.outputs
{'pushed_model': Channel( type_name: PushedModel artifacts: [Artifact(artifact: id: 17 type_id: 32 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/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/tmpkvhhk5j5/serving_model/taxi_simple/1640081600" } } custom_properties { key: "pushed_version" value { string_value: "1640081600" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 32 name: "PushedModel" )] additional_properties: {} additional_custom_properties: {} )}
En particular, el Pusher exportará su modelo en el formato SavedModel, que se ve así:
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)
('serving_default', <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31FDE50>) ('transform_features', <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31AC410>)
¡Hemos terminado nuestro recorrido por los componentes TFX integrados!