Uma introdução componente a componente ao TensorFlow Extended (TFX)
Ver no TensorFlow.org
Executar no Google Colab
Ver fonte no GitHub
Baixar cadernoEste tutorial baseado em Colab percorrerá interativamente cada componente integrado do TensorFlow Extended (TFX).
Ele cobre todas as etapas em um pipeline de aprendizado de máquina de ponta a ponta, desde a ingestão de dados até o envio de um modelo à exibição.
Quando terminar, o conteúdo deste bloco de notas pode ser exportado automaticamente como código-fonte do pipeline TFX, que você pode orquestrar com Apache Airflow e Apache Beam.
Fundo
Este bloco de notas demonstra como usar o TFX em um ambiente Jupyter / Colab. Aqui, examinamos o exemplo do Chicago Taxi em um notebook interativo.
Trabalhar em um notebook interativo é uma maneira útil de se familiarizar com a estrutura de um pipeline TFX. Também é útil ao desenvolver seus próprios pipelines como um ambiente de desenvolvimento leve, mas você deve estar ciente de que há diferenças na maneira como os blocos de anotações interativos são orquestrados e como acessam artefatos de metadados.
Orquestração
Em uma implantação de produção do TFX, você usará um orquestrador como Apache Airflow, Kubeflow Pipelines ou Apache Beam para orquestrar um gráfico de pipeline predefinido de componentes TFX. Em um notebook interativo, o próprio notebook é o orquestrador, executando cada componente TFX conforme você executa as células do notebook.
Metadados
Em uma implantação de produção do TFX, você acessará os metadados por meio da API ML Metadata (MLMD). O MLMD armazena propriedades de metadados em um banco de dados como MySQL ou SQLite e armazena as cargas de metadados em um armazenamento persistente, como em seu sistema de arquivos. Em um caderno interativo, ambas as propriedades e cargas são armazenadas em um banco de dados SQLite efêmera no /tmp diretório no notebook Jupyter ou servidor Colab.
Configurar
Primeiro, instalamos e importamos os pacotes necessários, configuramos caminhos e baixamos dados.
Atualizar Pip
Para evitar a atualização do Pip em um sistema quando executado localmente, verifique se estamos executando no Colab. É claro que os sistemas locais podem ser atualizados separadamente.
try:
import colab
!pip install --upgrade pip
except:
pass
Instale TFX
pip install -U tfx
Você reiniciou o tempo de execução?
Se você estiver usando o Google Colab, na primeira vez que executar a célula acima, será necessário reiniciar o tempo de execução (Tempo de execução> Reiniciar tempo de execução ...). Isso ocorre devido à maneira como o Colab carrega os pacotes.
Pacotes de importação
Importamos os pacotes necessários, incluindo classes de componentes TFX padrão.
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
Vamos verificar as versões da biblioteca.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.6.2 TFX version: 1.4.0
Configurar caminhos de pipeline
# 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)
Baixar dados de exemplo
Baixamos o conjunto de dados de exemplo para uso em nosso pipeline TFX.
O conjunto de dados que estamos usando é o Taxi Viagens conjunto de dados liberado pela cidade de Chicago. As colunas neste conjunto de dados são:
| 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 pagamento | companhia |
| trip_segundos | dropoff_community_area | pontas |
Com este conjunto de dados, vamos construir um modelo que prevê as tips de uma viagem.
_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-data6e4_3xo9/data.csv', <http.client.HTTPMessage at 0x7f1a7e8cfb10>)
Dê uma olhada rápida no arquivo 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
Isenção de responsabilidade: este site fornece aplicativos usando dados que foram modificados para uso de sua fonte original, www.cityofchicago.org, o site oficial da cidade de Chicago. A cidade de Chicago não faz nenhuma reclamação quanto ao conteúdo, precisão, oportunidade ou integridade de qualquer um dos dados fornecidos neste site. Os dados fornecidos neste site estão sujeitos a alterações a qualquer momento. Entende-se que os dados fornecidos neste site estão sendo utilizados por sua própria conta e risco.
Crie o InteractiveContext
Por último, criamos um InteractiveContext, que nos permitirá executar componentes TFX interativamente neste notebook.
# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4 as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/metadata.sqlite.
Executar componentes TFX interativamente
Nas células a seguir, criamos componentes TFX um por um, executamos cada um deles e visualizamos seus artefatos de saída.
ExampleGen
O ExampleGen componente é geralmente no início de um gasoduto TFX. Ele vai:
- Divida os dados em conjuntos de treinamento e avaliação (por padrão, 2/3 de treinamento + 1/3 de avaliação)
- Dados converter em
tf.Exampleformato (saiba mais aqui ) - Copiar os dados para o
_tfx_rootdiretório para outros componentes de acesso
ExampleGen toma como entrada o caminho para a fonte de dados. No nosso caso, este é o _data_root caminho que contém o CSV baixado.
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-data6e4_3xo9/* 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 examinar os artefatos de saída ExampleGen . Este componente produz dois artefatos, exemplos de treinamento e exemplos de avaliação:
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/CsvExampleGen/examples/1
Também podemos dar uma olhada nos três primeiros exemplos de treinamento:
# 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
}
}
}
}
Agora que ExampleGen terminou ingerindo os dados, o próximo passo é a análise de dados.
StatisticsGen
Os StatisticsGen Calcula componentes estatísticas sobre o conjunto de dados para análise de dados, bem como para uso em componentes a jusante. Ele usa o TensorFlow Validação de dados biblioteca.
StatisticsGen toma como entrada o conjunto de dados que apenas 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-2021-12-05T10_59_24.898354-se36qxc4/StatisticsGen/statistics/2/Split-train. INFO:absl:Generating statistics for split eval. INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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
Depois StatisticsGen termina a execução, podemos visualizar as estatísticas emitidas. Experimente brincar com os diferentes enredos!
context.show(statistics_gen.outputs['statistics'])
SchemaGen
O SchemaGen componente gera um esquema com base em suas estatísticas de dados. (Um esquema define limites do esperado, tipos e propriedades dos recursos do seu conjunto de dados.) Ele também usa o TensorFlow Validação de dados biblioteca.
SchemaGen terá como entrada as estatísticas que geramos com StatisticsGen , olhando para a divisão de treinamento por padrão.
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 WARNING: Logging before InitGoogleLogging() is written to STDERR I1205 10:59:36.632395 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type 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-2021-12-05T10_59_24.898354-se36qxc4/SchemaGen/schema/3/schema.pbtxt. INFO:absl:Running publisher for SchemaGen INFO:absl:MetadataStore with DB connection initialized
Depois SchemaGen terminar a execução, podemos visualizar o esquema gerado como uma tabela.
context.show(schema_gen.outputs['schema'])
Cada recurso em seu conjunto de dados aparece como uma linha na tabela de esquema, ao lado de suas propriedades. O esquema também captura todos os valores que um recurso categórico assume, denotados como seu domínio.
Para saber mais sobre esquemas, consulte a documentação SchemaGen .
Validador Exemplo
O ExampleValidator componente detecta anomalias em seus dados, com base nas expectativas definidas pelo esquema. Ele também usa o TensorFlow Validação de dados biblioteca.
ExampleValidator terá como entrada as estatísticas de StatisticsGen , eo 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-2021-12-05T10_59_24.898354-se36qxc4/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-2021-12-05T10_59_24.898354-se36qxc4/ExampleValidator/anomalies/4/Split-eval. INFO:absl:Running publisher for ExampleValidator INFO:absl:MetadataStore with DB connection initialized
Depois ExampleValidator terminar a execução, podemos visualizar as anomalias como uma mesa.
context.show(example_validator.outputs['anomalies'])
Na tabela de anomalias, podemos ver que não há anomalias. Isso é o que esperávamos, já que este é o primeiro conjunto de dados que analisamos e o esquema é personalizado para ele. Você deve revisar este esquema - qualquer coisa inesperada significa uma anomalia nos dados. Depois de revisado, o esquema pode ser usado para proteger dados futuros e as anomalias produzidas aqui podem ser usadas para depurar o desempenho do modelo, entender como seus dados evoluem ao longo do tempo e identificar erros de dados.
Transformar
A Transform executa componentes de engenharia recurso para treinamento e servir. Ele usa o TensorFlow Transform biblioteca.
Transform -se-á a entrada de dados a partir de ExampleGen , o esquema de SchemaGen , bem como um módulo que contém transformar código definido pelo utilizador.
Vamos ver um exemplo de transformação de código a seguir (para uma introdução ao TensorFlow Transform APIs, definido pelo utilizador ver o tutorial ). Primeiro, definimos algumas constantes para a engenharia de recursos:
_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
Em seguida, vamos escrever uma preprocessing_fn que leva em dados brutos como entrada, e retorna recursos transformados que o nosso modelo pode treinar em:
_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
Agora, passamos neste código de engenharia recurso para o Transform componente e executá-lo para transformar seus dados.
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_constants', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp6h4enzoj/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp1kilc09_', '--dist-dir', '/tmp/tmpu7dszvtp']
/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,
listing git files failed - pretending there aren't any
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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-2021-12-05T10_59_24.898354-se36qxc4/_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
I1205 10:59:37.233487 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Transform
I1205 10:59:37.237077 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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/tmp9ljjlr0t', '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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_constants.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmp1kilc09_
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmp1kilc09_
copying build/lib/taxi_transform.py -> /tmp/tmp1kilc09_
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmp1kilc09_/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3.7.egg-info
running install_scripts
creating /tmp/tmp1kilc09_/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/WHEEL
creating '/tmp/tmpu7dszvtp/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' and adding '/tmp/tmp1kilc09_' to it
adding 'taxi_constants.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/RECORD'
removing /tmp/tmp1kilc09_
Processing /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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-2021-12-05T10_59_24.898354-se36qxc4/_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-2021-12-05T10_59_24.898354-se36qxc4/_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/tmp6rcd17nh', '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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/tmpbq8i22l2', '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_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
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: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.
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: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: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.
2021-12-05 10:59:51.571461: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Transform/transform_graph/5/.temp_path/tftransform_tmp/7fa0435e7af949ef9e3b27e50d470602/assets
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Transform/transform_graph/5/.temp_path/tftransform_tmp/f9ed85f61d1f4528846646b3a922c30c/assets
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 examinar os artefatos de saída Transform . Este componente produz dois tipos de saídas:
-
transform_graphé o gráfico que pode realizar as operações de pré-processamento (este gráfico será incluído nos modelos servindo e avaliação). -
transformed_examplesrepresenta os dados de treinamento e avaliação pré-processadas.
transform.outputs
{'transform_graph': Channel(
type_name: TransformGraph
artifacts: [Artifact(artifact: id: 5
type_id: 22
uri: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
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-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
state: LIVE
, artifact_type: id: 14
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'updated_analyzer_cache': Channel(
type_name: TransformCache
artifacts: [Artifact(artifact: id: 7
type_id: 23
uri: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
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-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
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-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
state: LIVE
, artifact_type: id: 16
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_schema': Channel(
type_name: Schema
artifacts: [Artifact(artifact: id: 10
type_id: 18
uri: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
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-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
state: LIVE
, artifact_type: id: 16
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_anomalies': Channel(
type_name: ExampleAnomalies
artifacts: [Artifact(artifact: id: 12
type_id: 20
uri: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
state: LIVE
, artifact_type: id: 20
name: "ExampleAnomalies"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
)}
Dê uma olhada no transform_graph artefato. Ele aponta para um diretório contendo três subdiretórios.
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
O transformed_metadata subdiretório contém o esquema dos dados pré-processados. O transform_fn subdirectório contém o gráfico de pré-processamento real. O metadata subdiretório contém o esquema dos dados originais.
Também podemos dar uma olhada nos três primeiros exemplos 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: 0.06106060370802879
}
}
}
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: -0.15886740386486053
}
}
}
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: 1.2521241903305054
}
}
}
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: 0.3873794674873352
}
}
}
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: 0.21955278515815735
}
}
}
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
}
}
}
}
Após a Transform componente transformou seus dados em recursos, eo próximo passo é a formação de um modelo.
Treinador
O Trainer componente irá treinar um modelo que você define no TensorFlow (usando a API Estimador ou a API Keras com model_to_estimator ).
Trainer toma como entrada o esquema de SchemaGen , os dados transformados e gráfico de Transform , a formação de parâmetros, bem como um módulo que contém o código de modelo definido pelo utilizador.
Vamos ver um exemplo de modelo de código definido pelo usuário abaixo (para uma introdução ao estimador APIs TensorFlow, veja o tutorial ):
_taxi_trainer_module_file = 'taxi_trainer.py'
%%writefile {_taxi_trainer_module_file}
import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx_bsl.tfxio import dataset_options
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_MAX_CATEGORICAL_FEATURE_VALUES = taxi_constants.MAX_CATEGORICAL_FEATURE_VALUES
_LABEL_KEY = taxi_constants.LABEL_KEY
# Tf.Transform considers these features as "raw"
def _get_raw_feature_spec(schema):
return schema_utils.schema_as_feature_spec(schema).feature_spec
def _build_estimator(config, hidden_units=None, warm_start_from=None):
"""Build an estimator for predicting the tipping behavior of taxi riders.
Args:
config: tf.estimator.RunConfig defining the runtime environment for the
estimator (including model_dir).
hidden_units: [int], the layer sizes of the DNN (input layer first)
warm_start_from: Optional directory to warm start from.
Returns:
A dict of the following:
- estimator: The estimator that will be used for training and eval.
- train_spec: Spec for training.
- eval_spec: Spec for eval.
- eval_input_receiver_fn: Input function for eval.
"""
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _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)
]
return tf.estimator.DNNLinearCombinedClassifier(
config=config,
linear_feature_columns=categorical_columns,
dnn_feature_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25],
warm_start_from=warm_start_from)
def _example_serving_receiver_fn(tf_transform_graph, schema):
"""Build the serving in inputs.
Args:
tf_transform_graph: A TFTransformOutput.
schema: the schema of the input data.
Returns:
Tensorflow graph which parses examples, applying tf-transform to them.
"""
raw_feature_spec = _get_raw_feature_spec(schema)
raw_feature_spec.pop(_LABEL_KEY)
raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
raw_feature_spec, default_batch_size=None)
serving_input_receiver = raw_input_fn()
transformed_features = tf_transform_graph.transform_raw_features(
serving_input_receiver.features)
return tf.estimator.export.ServingInputReceiver(
transformed_features, serving_input_receiver.receiver_tensors)
def _eval_input_receiver_fn(tf_transform_graph, schema):
"""Build everything needed for the tf-model-analysis to run the model.
Args:
tf_transform_graph: A TFTransformOutput.
schema: the schema of the input data.
Returns:
EvalInputReceiver function, which contains:
- Tensorflow graph which parses raw untransformed features, applies the
tf-transform preprocessing operators.
- Set of raw, untransformed features.
- Label against which predictions will be compared.
"""
# Notice that the inputs are raw features, not transformed features here.
raw_feature_spec = _get_raw_feature_spec(schema)
serialized_tf_example = tf.compat.v1.placeholder(
dtype=tf.string, shape=[None], name='input_example_tensor')
# Add a parse_example operator to the tensorflow graph, which will parse
# raw, untransformed, tf examples.
features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
# Now that we have our raw examples, process them through the tf-transform
# function computed during the preprocessing step.
transformed_features = tf_transform_graph.transform_raw_features(
features)
# The key name MUST be 'examples'.
receiver_tensors = {'examples': serialized_tf_example}
# NOTE: Model is driven by transformed features (since training works on the
# materialized output of TFT, but slicing will happen on raw features.
features.update(transformed_features)
return tfma.export.EvalInputReceiver(
features=features,
receiver_tensors=receiver_tensors,
labels=transformed_features[_LABEL_KEY])
def _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size=200):
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_LABEL_KEY),
tf_transform_output.transformed_metadata.schema)
# TFX will call this function
def trainer_fn(trainer_fn_args, schema):
"""Build the estimator using the high level API.
Args:
trainer_fn_args: Holds args used to train the model as name/value pairs.
schema: Holds the schema of the training examples.
Returns:
A dict of the following:
- estimator: The estimator that will be used for training and eval.
- train_spec: Spec for training.
- eval_spec: Spec for eval.
- eval_input_receiver_fn: Input function for eval.
"""
# Number of nodes in the first layer of the DNN
first_dnn_layer_size = 100
num_dnn_layers = 4
dnn_decay_factor = 0.7
train_batch_size = 40
eval_batch_size = 40
tf_transform_graph = tft.TFTransformOutput(trainer_fn_args.transform_output)
train_input_fn = lambda: _input_fn( # pylint: disable=g-long-lambda
trainer_fn_args.train_files,
trainer_fn_args.data_accessor,
tf_transform_graph,
batch_size=train_batch_size)
eval_input_fn = lambda: _input_fn( # pylint: disable=g-long-lambda
trainer_fn_args.eval_files,
trainer_fn_args.data_accessor,
tf_transform_graph,
batch_size=eval_batch_size)
train_spec = tf.estimator.TrainSpec( # pylint: disable=g-long-lambda
train_input_fn,
max_steps=trainer_fn_args.train_steps)
serving_receiver_fn = lambda: _example_serving_receiver_fn( # pylint: disable=g-long-lambda
tf_transform_graph, schema)
exporter = tf.estimator.FinalExporter('chicago-taxi', serving_receiver_fn)
eval_spec = tf.estimator.EvalSpec(
eval_input_fn,
steps=trainer_fn_args.eval_steps,
exporters=[exporter],
name='chicago-taxi-eval')
run_config = tf.estimator.RunConfig(
save_checkpoints_steps=999, keep_checkpoint_max=1)
run_config = run_config.replace(model_dir=trainer_fn_args.serving_model_dir)
estimator = _build_estimator(
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
for i in range(num_dnn_layers)
],
config=run_config,
warm_start_from=trainer_fn_args.base_model)
# Create an input receiver for TFMA processing
receiver_fn = lambda: _eval_input_receiver_fn( # pylint: disable=g-long-lambda
tf_transform_graph, schema)
return {
'estimator': estimator,
'train_spec': train_spec,
'eval_spec': eval_spec,
'eval_input_receiver_fn': receiver_fn
}
Writing taxi_trainer.py
Agora, passamos neste código modelo para o Trainer componente e executá-lo para treinar o modelo.
from tfx.components.trainer.executor import Executor
from tfx.dsl.components.base import executor_spec
trainer = tfx.components.Trainer(
module_file=os.path.abspath(_taxi_trainer_module_file),
custom_executor_spec=executor_spec.ExecutorClassSpec(Executor),
examples=transform.outputs['transformed_examples'],
schema=schema_gen.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=tfx.proto.TrainArgs(num_steps=10000),
eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
WARNING:absl:`custom_executor_spec` is deprecated. Please customize component directly.
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_constants', 'taxi_trainer', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpfdfqeq3n/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmplwndr27q', '--dist-dir', '/tmp/tmpm5jkf1c7']
/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,
listing git files failed - pretending there aren't any
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
I1205 11:00:05.421522 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Trainer
I1205 11:00:05.425110 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'} 'trainer_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpudspobnm', '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_trainer.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmplwndr27q
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmplwndr27q
copying build/lib/taxi_transform.py -> /tmp/tmplwndr27q
copying build/lib/taxi_trainer.py -> /tmp/tmplwndr27q
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmplwndr27q/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3.7.egg-info
running install_scripts
creating /tmp/tmplwndr27q/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/WHEEL
creating '/tmp/tmpm5jkf1c7/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl' and adding '/tmp/tmplwndr27q' to it
adding 'taxi_constants.py'
adding 'taxi_trainer.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/RECORD'
removing /tmp/tmplwndr27q
Processing /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:absl:Training model.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:absl:Feature company 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:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/engine/base_layer_v1.py:1684: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
warnings.warn('`layer.add_variable` is deprecated and '
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adagrad.py:84: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.7078417, step = 0
INFO:tensorflow:global_step/sec: 70.3244
INFO:tensorflow:loss = 0.54806644, step = 100 (1.423 sec)
INFO:tensorflow:global_step/sec: 86.7936
INFO:tensorflow:loss = 0.61360043, step = 200 (1.152 sec)
INFO:tensorflow:global_step/sec: 85.3687
INFO:tensorflow:loss = 0.4860243, step = 300 (1.171 sec)
INFO:tensorflow:global_step/sec: 86.4491
INFO:tensorflow:loss = 0.4932023, step = 400 (1.157 sec)
INFO:tensorflow:global_step/sec: 84.918
INFO:tensorflow:loss = 0.41420126, step = 500 (1.177 sec)
INFO:tensorflow:global_step/sec: 85.5433
INFO:tensorflow:loss = 0.502645, step = 600 (1.169 sec)
INFO:tensorflow:global_step/sec: 85.7348
INFO:tensorflow:loss = 0.5135077, step = 700 (1.166 sec)
INFO:tensorflow:global_step/sec: 85.9959
INFO:tensorflow:loss = 0.50064766, step = 800 (1.163 sec)
INFO:tensorflow:global_step/sec: 84.4301
INFO:tensorflow:loss = 0.5338023, step = 900 (1.185 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:absl:Feature company 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:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-12-05T11:00:25
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 45.25082s
INFO:tensorflow:Finished evaluation at 2021-12-05-11:01:10
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.77114, accuracy_baseline = 0.77114, auc = 0.92330086, auc_precision_recall = 0.66446304, average_loss = 0.46160534, global_step = 999, label/mean = 0.22886, loss = 0.46160552, precision = 0.0, prediction/mean = 0.24982427, recall = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 2.07624
INFO:tensorflow:loss = 0.5403578, step = 1000 (48.163 sec)
INFO:tensorflow:global_step/sec: 86.3781
INFO:tensorflow:loss = 0.38168782, step = 1100 (1.158 sec)
INFO:tensorflow:global_step/sec: 85.2624
INFO:tensorflow:loss = 0.39346403, step = 1200 (1.173 sec)
INFO:tensorflow:global_step/sec: 83.7912
INFO:tensorflow:loss = 0.40447283, step = 1300 (1.194 sec)
INFO:tensorflow:global_step/sec: 84.0061
INFO:tensorflow:loss = 0.44532022, step = 1400 (1.190 sec)
INFO:tensorflow:global_step/sec: 85.6364
INFO:tensorflow:loss = 0.44722432, step = 1500 (1.169 sec)
INFO:tensorflow:global_step/sec: 86.1981
INFO:tensorflow:loss = 0.38483262, step = 1600 (1.159 sec)
INFO:tensorflow:global_step/sec: 86.8631
INFO:tensorflow:loss = 0.5259759, step = 1700 (1.152 sec)
INFO:tensorflow:global_step/sec: 84.9455
INFO:tensorflow:loss = 0.55505085, step = 1800 (1.177 sec)
INFO:tensorflow:global_step/sec: 86.3588
INFO:tensorflow:loss = 0.38577095, step = 1900 (1.158 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 78.271
INFO:tensorflow:loss = 0.5068237, step = 2000 (1.277 sec)
INFO:tensorflow:global_step/sec: 86.0626
INFO:tensorflow:loss = 0.43203792, step = 2100 (1.162 sec)
INFO:tensorflow:global_step/sec: 84.691
INFO:tensorflow:loss = 0.4243142, step = 2200 (1.181 sec)
INFO:tensorflow:global_step/sec: 86.057
INFO:tensorflow:loss = 0.33626375, step = 2300 (1.162 sec)
INFO:tensorflow:global_step/sec: 86.4836
INFO:tensorflow:loss = 0.5215112, step = 2400 (1.156 sec)
INFO:tensorflow:global_step/sec: 86.1571
INFO:tensorflow:loss = 0.3480332, step = 2500 (1.161 sec)
INFO:tensorflow:global_step/sec: 83.5733
INFO:tensorflow:loss = 0.3900601, step = 2600 (1.197 sec)
INFO:tensorflow:global_step/sec: 85.2641
INFO:tensorflow:loss = 0.41936797, step = 2700 (1.174 sec)
INFO:tensorflow:global_step/sec: 84.707
INFO:tensorflow:loss = 0.37252873, step = 2800 (1.179 sec)
INFO:tensorflow:global_step/sec: 84.4798
INFO:tensorflow:loss = 0.38240016, step = 2900 (1.184 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 75.8418
INFO:tensorflow:loss = 0.2528301, step = 3000 (1.318 sec)
INFO:tensorflow:global_step/sec: 84.156
INFO:tensorflow:loss = 0.4254836, step = 3100 (1.188 sec)
INFO:tensorflow:global_step/sec: 85.0661
INFO:tensorflow:loss = 0.5024188, step = 3200 (1.176 sec)
INFO:tensorflow:global_step/sec: 82.2437
INFO:tensorflow:loss = 0.3909358, step = 3300 (1.216 sec)
INFO:tensorflow:global_step/sec: 82.2637
INFO:tensorflow:loss = 0.328662, step = 3400 (1.216 sec)
INFO:tensorflow:global_step/sec: 84.4683
INFO:tensorflow:loss = 0.36957046, step = 3500 (1.184 sec)
INFO:tensorflow:global_step/sec: 84.4389
INFO:tensorflow:loss = 0.43177825, step = 3600 (1.184 sec)
INFO:tensorflow:global_step/sec: 85.2814
INFO:tensorflow:loss = 0.43844128, step = 3700 (1.173 sec)
INFO:tensorflow:global_step/sec: 83.9934
INFO:tensorflow:loss = 0.3894402, step = 3800 (1.191 sec)
INFO:tensorflow:global_step/sec: 85.6644
INFO:tensorflow:loss = 0.3499531, step = 3900 (1.167 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 77.2294
INFO:tensorflow:loss = 0.43472967, step = 4000 (1.294 sec)
INFO:tensorflow:global_step/sec: 86.9355
INFO:tensorflow:loss = 0.31338528, step = 4100 (1.151 sec)
INFO:tensorflow:global_step/sec: 86.7796
INFO:tensorflow:loss = 0.45728058, step = 4200 (1.152 sec)
INFO:tensorflow:global_step/sec: 86.8483
INFO:tensorflow:loss = 0.39699784, step = 4300 (1.151 sec)
INFO:tensorflow:global_step/sec: 87.1248
INFO:tensorflow:loss = 0.43616992, step = 4400 (1.148 sec)
INFO:tensorflow:global_step/sec: 86.8816
INFO:tensorflow:loss = 0.35230064, step = 4500 (1.151 sec)
INFO:tensorflow:global_step/sec: 86.9788
INFO:tensorflow:loss = 0.36814964, step = 4600 (1.150 sec)
INFO:tensorflow:global_step/sec: 86.884
INFO:tensorflow:loss = 0.39265686, step = 4700 (1.151 sec)
INFO:tensorflow:global_step/sec: 86.3142
INFO:tensorflow:loss = 0.3569767, step = 4800 (1.159 sec)
INFO:tensorflow:global_step/sec: 86.7831
INFO:tensorflow:loss = 0.38372093, step = 4900 (1.152 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 77.8516
INFO:tensorflow:loss = 0.37753737, step = 5000 (1.284 sec)
INFO:tensorflow:global_step/sec: 86.9472
INFO:tensorflow:loss = 0.39870018, step = 5100 (1.150 sec)
INFO:tensorflow:global_step/sec: 87.6235
INFO:tensorflow:loss = 0.3469496, step = 5200 (1.141 sec)
INFO:tensorflow:global_step/sec: 85.5072
INFO:tensorflow:loss = 0.4431352, step = 5300 (1.169 sec)
INFO:tensorflow:global_step/sec: 86.753
INFO:tensorflow:loss = 0.4120473, step = 5400 (1.153 sec)
INFO:tensorflow:global_step/sec: 87.9292
INFO:tensorflow:loss = 0.41318005, step = 5500 (1.137 sec)
INFO:tensorflow:global_step/sec: 86.9944
INFO:tensorflow:loss = 0.33395153, step = 5600 (1.150 sec)
INFO:tensorflow:global_step/sec: 85.7159
INFO:tensorflow:loss = 0.39095598, step = 5700 (1.167 sec)
INFO:tensorflow:global_step/sec: 86.5248
INFO:tensorflow:loss = 0.3990689, step = 5800 (1.156 sec)
INFO:tensorflow:global_step/sec: 87.7908
INFO:tensorflow:loss = 0.35857546, step = 5900 (1.139 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 77.8735
INFO:tensorflow:loss = 0.3701624, step = 6000 (1.284 sec)
INFO:tensorflow:global_step/sec: 87.5076
INFO:tensorflow:loss = 0.41708413, step = 6100 (1.143 sec)
INFO:tensorflow:global_step/sec: 84.466
INFO:tensorflow:loss = 0.29821724, step = 6200 (1.184 sec)
INFO:tensorflow:global_step/sec: 83.526
INFO:tensorflow:loss = 0.35562894, step = 6300 (1.197 sec)
INFO:tensorflow:global_step/sec: 87.5455
INFO:tensorflow:loss = 0.28250116, step = 6400 (1.142 sec)
INFO:tensorflow:global_step/sec: 86.3403
INFO:tensorflow:loss = 0.3280113, step = 6500 (1.158 sec)
INFO:tensorflow:global_step/sec: 87.024
INFO:tensorflow:loss = 0.3482268, step = 6600 (1.149 sec)
INFO:tensorflow:global_step/sec: 85.355
INFO:tensorflow:loss = 0.37907737, step = 6700 (1.172 sec)
INFO:tensorflow:global_step/sec: 84.621
INFO:tensorflow:loss = 0.31550306, step = 6800 (1.182 sec)
INFO:tensorflow:global_step/sec: 83.3363
INFO:tensorflow:loss = 0.3832593, step = 6900 (1.202 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 74.4455
INFO:tensorflow:loss = 0.41803008, step = 7000 (1.342 sec)
INFO:tensorflow:global_step/sec: 82.7229
INFO:tensorflow:loss = 0.32837537, step = 7100 (1.209 sec)
INFO:tensorflow:global_step/sec: 84.3715
INFO:tensorflow:loss = 0.33435482, step = 7200 (1.185 sec)
INFO:tensorflow:global_step/sec: 84.2735
INFO:tensorflow:loss = 0.26065814, step = 7300 (1.187 sec)
INFO:tensorflow:global_step/sec: 85.663
INFO:tensorflow:loss = 0.41420022, step = 7400 (1.167 sec)
INFO:tensorflow:global_step/sec: 87.0079
INFO:tensorflow:loss = 0.40608707, step = 7500 (1.150 sec)
INFO:tensorflow:global_step/sec: 87.7408
INFO:tensorflow:loss = 0.36437988, step = 7600 (1.140 sec)
INFO:tensorflow:global_step/sec: 87.4937
INFO:tensorflow:loss = 0.39505738, step = 7700 (1.144 sec)
INFO:tensorflow:global_step/sec: 88.4098
INFO:tensorflow:loss = 0.2943158, step = 7800 (1.130 sec)
INFO:tensorflow:global_step/sec: 87.3161
INFO:tensorflow:loss = 0.352277, step = 7900 (1.145 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 78.0033
INFO:tensorflow:loss = 0.24916664, step = 8000 (1.282 sec)
INFO:tensorflow:global_step/sec: 87.818
INFO:tensorflow:loss = 0.23849675, step = 8100 (1.139 sec)
INFO:tensorflow:global_step/sec: 86.7864
INFO:tensorflow:loss = 0.35711345, step = 8200 (1.152 sec)
INFO:tensorflow:global_step/sec: 87.5709
INFO:tensorflow:loss = 0.3992316, step = 8300 (1.142 sec)
INFO:tensorflow:global_step/sec: 86.4715
INFO:tensorflow:loss = 0.38699418, step = 8400 (1.157 sec)
INFO:tensorflow:global_step/sec: 87.1347
INFO:tensorflow:loss = 0.27517205, step = 8500 (1.147 sec)
INFO:tensorflow:global_step/sec: 87.6778
INFO:tensorflow:loss = 0.3764573, step = 8600 (1.140 sec)
INFO:tensorflow:global_step/sec: 86.488
INFO:tensorflow:loss = 0.38588572, step = 8700 (1.156 sec)
INFO:tensorflow:global_step/sec: 88.0878
INFO:tensorflow:loss = 0.34926754, step = 8800 (1.135 sec)
INFO:tensorflow:global_step/sec: 86.5916
INFO:tensorflow:loss = 0.3552958, step = 8900 (1.155 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 75.4932
INFO:tensorflow:loss = 0.36349216, step = 9000 (1.325 sec)
INFO:tensorflow:global_step/sec: 83.4161
INFO:tensorflow:loss = 0.35490102, step = 9100 (1.199 sec)
INFO:tensorflow:global_step/sec: 87.0142
INFO:tensorflow:loss = 0.36661166, step = 9200 (1.149 sec)
INFO:tensorflow:global_step/sec: 86.8802
INFO:tensorflow:loss = 0.42985326, step = 9300 (1.151 sec)
INFO:tensorflow:global_step/sec: 87.3449
INFO:tensorflow:loss = 0.47281235, step = 9400 (1.145 sec)
INFO:tensorflow:global_step/sec: 88.3826
INFO:tensorflow:loss = 0.22590041, step = 9500 (1.131 sec)
INFO:tensorflow:global_step/sec: 87.3166
INFO:tensorflow:loss = 0.4162217, step = 9600 (1.145 sec)
INFO:tensorflow:global_step/sec: 87.5265
INFO:tensorflow:loss = 0.37611717, step = 9700 (1.143 sec)
INFO:tensorflow:global_step/sec: 86.1899
INFO:tensorflow:loss = 0.3856167, step = 9800 (1.160 sec)
INFO:tensorflow:global_step/sec: 87.7519
INFO:tensorflow:loss = 0.24105208, step = 9900 (1.140 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:absl:Feature company 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:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-12-05T11:02:58
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 43.13040s
INFO:tensorflow:Finished evaluation at 2021-12-05-11:03:41
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.787805, accuracy_baseline = 0.771235, auc = 0.9339468, auc_precision_recall = 0.70544505, average_loss = 0.3452758, global_step = 10000, label/mean = 0.228765, loss = 0.34527487, precision = 0.69398266, prediction/mean = 0.2301482, recall = 0.12956527
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:145: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1638702221/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1638702221/saved_model.pb
INFO:tensorflow:Loss for final step: 0.3770034.
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6
INFO:absl:Exporting eval_savedmodel for TFMA.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-TFMA/temp-1638702224/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-TFMA/temp-1638702224/saved_model.pb
INFO:absl:Exported eval_savedmodel to /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model_run/6/Format-TFMA.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/serving_model_dir/saved_model.pb"
INFO:absl:Serving model copied to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model/6/Format-Serving.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/eval_model_dir/saved_model.pb"
INFO:absl:Eval model copied to: /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model/6/Format-TFMA.
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized
Analise o treinamento com o TensorBoard
Opcionalmente, podemos conectar o TensorBoard ao Trainer para analisar as curvas de treinamento do nosso modelo.
# Get the URI of the output artifact representing the training logs, which is a directory
model_run_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_dir}
Avaliador
O Evaluator componente calcula métricas de desempenho modelo sobre o conjunto da avaliação. Ele usa o TensorFlow Modelo de Análise de biblioteca. O Evaluator também pode validar opcionalmente que um modelo recém-formados é melhor do que o modelo anterior. Isso é útil em uma configuração de pipeline de produção, onde você pode treinar e validar automaticamente um modelo todos os dias. Neste caderno, nós só treinar um modelo, de modo que o Evaluator irá automaticamente rotular o modelo como "bom".
Evaluator terá como entrada a dados de ExampleGen , o modelo treinado desde Trainer e configuração de corte. A configuração de fatiamento permite que você divida suas métricas em valores de recursos (por exemplo, como seu modelo se sai em viagens de táxi que começam às 8h versus 20h?). Veja um exemplo dessa configuração abaixo:
eval_config = tfma.EvalConfig(
model_specs=[
# Using signature 'eval' implies the use of an EvalSavedModel. To use
# a serving model remove the signature to defaults to 'serving_default'
# and add a label_key.
tfma.ModelSpec(signature_name='eval')
],
metrics_specs=[
tfma.MetricsSpec(
# The metrics added here are in addition to those saved with the
# model (assuming either a keras model or EvalSavedModel is used).
# Any metrics added into the saved model (for example using
# model.compile(..., metrics=[...]), etc) will be computed
# automatically.
metrics=[
tfma.MetricConfig(class_name='ExampleCount')
],
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
thresholds = {
'accuracy': tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.5}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
}
)
],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Data can be sliced along a feature column. In this case, data is
# sliced along feature column trip_start_hour.
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
Em seguida, damos esta configuração para Evaluator e executá-lo.
# 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'],
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
I1205 11:03:46.279654 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Evaluator
I1205 11:03:46.282887 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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 "thresholds": {\n "accuracy": {\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 "model_specs": [\n {\n "signature_name": "eval"\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: "eval"
}
slicing_specs {
}
slicing_specs {
feature_keys: "trip_start_hour"
}
metrics_specs {
metrics {
class_name: "ExampleCount"
}
thresholds {
key: "accuracy"
value {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
}
INFO:absl:Using /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model/6/Format-TFMA as model.
WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
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 "thresholds": {\n "accuracy": {\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 "model_specs": [\n {\n "signature_name": "eval"\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: "eval"
}
slicing_specs {
}
slicing_specs {
feature_keys: "trip_start_hour"
}
metrics_specs {
metrics {
class_name: "ExampleCount"
}
model_names: ""
thresholds {
key: "accuracy"
value {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
}
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
feature_keys: "trip_start_hour"
}
metrics_specs {
metrics {
class_name: "ExampleCount"
}
model_names: ""
thresholds {
key: "accuracy"
value {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
}
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
feature_keys: "trip_start_hour"
}
metrics_specs {
metrics {
class_name: "ExampleCount"
}
model_names: ""
thresholds {
key: "accuracy"
value {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
}
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:169: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Trainer/model/6/Format-TFMA/variables/variables
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:189: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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:114: 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-2021-12-05T10_59_24.898354-se36qxc4/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized
Agora vamos examinar os artefatos de saída Evaluator .
evaluator.outputs
{'evaluation': Channel(
type_name: ModelEvaluation
artifacts: [Artifact(artifact: id: 15
type_id: 29
uri: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
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-2021-12-05T10_59_24.898354-se36qxc4/Evaluator/blessing/8"
custom_properties {
key: "blessed"
value {
int_value: 1
}
}
custom_properties {
key: "current_model"
value {
string_value: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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: "1.4.0"
}
}
state: LIVE
, artifact_type: id: 30
name: "ModelBlessing"
)]
additional_properties: {}
additional_custom_properties: {}
)}
Utilizando a evaluation de saída podemos mostrar a visualização padrão de métricas globais em todo o conjunto de avaliação.
context.show(evaluator.outputs['evaluation'])
Para ver a visualização das métricas de avaliação em fatias, podemos chamar diretamente a biblioteca TensorFlow Model Analysis.
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 visualização mostra as mesmas métricas, mas calculado a cada valor de característica do trip_start_hour em vez de todo o conjunto de avaliação.
A Análise de modelo do TensorFlow oferece suporte a muitas outras visualizações, como indicadores de imparcialidade e plotagem de uma série temporal de desempenho do modelo. Para saber mais, consulte o tutorial .
Como adicionamos limites à nossa configuração, a saída de validação também está disponível. O precence de uma blessing artefato indica que nosso modelo passou na validação. Como esta é a primeira validação realizada, o candidato é automaticamente abençoado.
blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0 -rw-rw-r-- 1 kbuilder kbuilder 0 Dec 5 11:03 BLESSED
Agora também pode verificar o sucesso carregando o registro do resultado da validação:
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
}
}
Empurrador
O Pusher componente é geralmente no final de um oleoduto TFX. Ele verifica se um modelo passou na validação, e em caso afirmativo, as exportações do modelo para _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 I1205 11:03:54.694877 1805 rdbms_metadata_access_object.cc:686] No property is defined for the Type INFO:absl:Running executor for Pusher INFO:absl:Model version: 1638702234 INFO:absl:Model written to serving path /tmp/tmposmo4233/serving_model/taxi_simple/1638702234. INFO:absl:Model pushed to /tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/Pusher/pushed_model/9. INFO:absl:Running publisher for Pusher INFO:absl:MetadataStore with DB connection initialized
Vamos examinar os artefatos de saída Pusher .
pusher.outputs
{'pushed_model': Channel(
type_name: PushedModel
artifacts: [Artifact(artifact: id: 17
type_id: 32
uri: "/tmp/tfx-interactive-2021-12-05T10_59_24.898354-se36qxc4/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/tmposmo4233/serving_model/taxi_simple/1638702234"
}
}
custom_properties {
key: "pushed_version"
value {
string_value: "1638702234"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
, artifact_type: id: 32
name: "PushedModel"
)]
additional_properties: {}
additional_custom_properties: {}
)}
Em particular, o Pusher exportará seu modelo no formato SavedModel, que se parece com isto:
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)
('regression', <ConcreteFunction pruned(inputs) at 0x7F19BF0F9510>)
('classification', <ConcreteFunction pruned(inputs) at 0x7F19BE0EC350>)
('serving_default', <ConcreteFunction pruned(inputs) at 0x7F19BC6BE210>)
('predict', <ConcreteFunction pruned(examples) at 0x7F19BC4F9090>)
Terminamos nosso tour pelos componentes integrados do TFX!