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Demonstração do kit de ferramentas de cartão de modelo MLMD

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Fundo

Este bloco de notas demonstra como gerar um cartão de modelo usando o Model Card Toolkit com MLMD e pipeline TFX em um ambiente Jupyter / Colab. Você pode aprender mais sobre modelos de cartões em https://modelcards.withgoogle.com/about

Configurar

Primeiro precisamos a) instalar e importar os pacotes necessários eb) baixar os dados.

Atualize para o Pip 20.2 e instale o TFX

pip install -q --upgrade pip==20.2
pip install -q "tfx==0.26.0"
pip install -q model-card-toolkit

Você reiniciou o tempo de execução?

Se você estiver usando o Google Colab, na primeira vez que executar a célula acima, deverá reiniciar o tempo de execução (Tempo de execução> Reiniciar tempo de execução ...). Isso se deve à maneira como o Colab carrega os pacotes.

Pacotes de importação

Importamos os pacotes necessários, incluindo classes de componentes TFX padrão e verificamos as versões da biblioteca.

import os
import pprint
import tempfile
import urllib

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

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

import ml_metadata as mlmd
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.version.__version__))
print('MLMD version: {}'.format(mlmd.__version__))
TensorFlow version: 2.3.2
TFX version: 0.26.0
MLMD version: 0.26.0

Configurar caminhos de pipeline

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

# 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.

DATA_PATH = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/' \
   'adult.data'
_data_root = tempfile.mkdtemp(prefix='tfx-data')
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)

columns = [
  "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Marital-Status",
  "Occupation", "Relationship", "Race", "Sex", "Capital-Gain", "Capital-Loss",
  "Hours-per-week", "Country", "Over-50K"]

with open(_data_filepath, 'r') as f:
  content = f.read()
  content = content.replace(", <=50K", ', 0').replace(", >50K", ', 1')

with open(_data_filepath, 'w') as f:
  f.write(','.join(columns) + '\n' + content)

Dê uma olhada rápida no arquivo CSV.

head {_data_filepath}
Age,Workclass,fnlwgt,Education,Education-Num,Marital-Status,Occupation,Relationship,Race,Sex,Capital-Gain,Capital-Loss,Hours-per-week,Country,Over-50K
39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, 0
50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, 0
38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0
53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, 0
28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, 0
37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, 0
49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, 0
52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, 1
31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, 1

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-2021-02-05T22_01_17.129037-teowo3b1 as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/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. Neste caderno, que não irá fornecer explicações detalhadas de cada componente TFX, mas você pode ver o que cada um faz na oficina TFX Colab .

ExampleGen

Criar o ExampleGen componente aos dados divididos em conjuntos de treinamento e avaliação, converter os dados em tf.Example formato e copiar dados para o _tfx_root diretório para outros componentes de acesso.

example_gen = CsvExampleGen(input=external_input(_data_root))
context.run(example_gen)
WARNING:absl:From <ipython-input-1-2e0190c2dd16>:1: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.
Instructions for updating:
external_input is deprecated, directly pass the uri to ExampleGen.
WARNING:absl:The "input" argument to the CsvExampleGen component has been deprecated by "input_base". Please update your usage as support for this argument will be removed soon.
INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-datajjx_v0dr/* to TFExample.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/CsvExampleGen/examples/1

Vamos 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, '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: "Age"
    value {
      int64_list {
        value: 39
      }
    }
  }
  feature {
    key: "Capital-Gain"
    value {
      int64_list {
        value: 2174
      }
    }
  }
  feature {
    key: "Capital-Loss"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Country"
    value {
      bytes_list {
        value: " United-States"
      }
    }
  }
  feature {
    key: "Education"
    value {
      bytes_list {
        value: " Bachelors"
      }
    }
  }
  feature {
    key: "Education-Num"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "Hours-per-week"
    value {
      int64_list {
        value: 40
      }
    }
  }
  feature {
    key: "Marital-Status"
    value {
      bytes_list {
        value: " Never-married"
      }
    }
  }
  feature {
    key: "Occupation"
    value {
      bytes_list {
        value: " Adm-clerical"
      }
    }
  }
  feature {
    key: "Over-50K"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Race"
    value {
      bytes_list {
        value: " White"
      }
    }
  }
  feature {
    key: "Relationship"
    value {
      bytes_list {
        value: " Not-in-family"
      }
    }
  }
  feature {
    key: "Sex"
    value {
      bytes_list {
        value: " Male"
      }
    }
  }
  feature {
    key: "Workclass"
    value {
      bytes_list {
        value: " State-gov"
      }
    }
  }
  feature {
    key: "fnlwgt"
    value {
      int64_list {
        value: 77516
      }
    }
  }
}

features {
  feature {
    key: "Age"
    value {
      int64_list {
        value: 50
      }
    }
  }
  feature {
    key: "Capital-Gain"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Capital-Loss"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Country"
    value {
      bytes_list {
        value: " United-States"
      }
    }
  }
  feature {
    key: "Education"
    value {
      bytes_list {
        value: " Bachelors"
      }
    }
  }
  feature {
    key: "Education-Num"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "Hours-per-week"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "Marital-Status"
    value {
      bytes_list {
        value: " Married-civ-spouse"
      }
    }
  }
  feature {
    key: "Occupation"
    value {
      bytes_list {
        value: " Exec-managerial"
      }
    }
  }
  feature {
    key: "Over-50K"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Race"
    value {
      bytes_list {
        value: " White"
      }
    }
  }
  feature {
    key: "Relationship"
    value {
      bytes_list {
        value: " Husband"
      }
    }
  }
  feature {
    key: "Sex"
    value {
      bytes_list {
        value: " Male"
      }
    }
  }
  feature {
    key: "Workclass"
    value {
      bytes_list {
        value: " Self-emp-not-inc"
      }
    }
  }
  feature {
    key: "fnlwgt"
    value {
      int64_list {
        value: 83311
      }
    }
  }
}

features {
  feature {
    key: "Age"
    value {
      int64_list {
        value: 38
      }
    }
  }
  feature {
    key: "Capital-Gain"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Capital-Loss"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Country"
    value {
      bytes_list {
        value: " United-States"
      }
    }
  }
  feature {
    key: "Education"
    value {
      bytes_list {
        value: " HS-grad"
      }
    }
  }
  feature {
    key: "Education-Num"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "Hours-per-week"
    value {
      int64_list {
        value: 40
      }
    }
  }
  feature {
    key: "Marital-Status"
    value {
      bytes_list {
        value: " Divorced"
      }
    }
  }
  feature {
    key: "Occupation"
    value {
      bytes_list {
        value: " Handlers-cleaners"
      }
    }
  }
  feature {
    key: "Over-50K"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Race"
    value {
      bytes_list {
        value: " White"
      }
    }
  }
  feature {
    key: "Relationship"
    value {
      bytes_list {
        value: " Not-in-family"
      }
    }
  }
  feature {
    key: "Sex"
    value {
      bytes_list {
        value: " Male"
      }
    }
  }
  feature {
    key: "Workclass"
    value {
      bytes_list {
        value: " Private"
      }
    }
  }
  feature {
    key: "fnlwgt"
    value {
      int64_list {
        value: 215646
      }
    }
  }
}

StatisticsGen

StatisticsGen toma como entrada o conjunto de dados que apenas ingerido usando ExampleGen e permite que você execute uma análise do conjunto de dados usando TensorFlow Validação de dados (TFDV).

statistics_gen = StatisticsGen(
    examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/StatisticsGen/statistics/2/train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/StatisticsGen/statistics/2/eval.
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'])
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_util.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

SchemaGen

SchemaGen terá como entrada as estatísticas que geramos com StatisticsGen , olhando para a divisão de treinamento por padrão.

schema_gen = SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
context.show(schema_gen.outputs['schema'])
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/display_util.py:151: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

Para saber mais sobre esquemas, consulte a documentação SchemaGen .

Transformar

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 ).

_census_income_constants_module_file = 'census_income_constants.py'

Writing census_income_constants.py
_census_income_transform_module_file = 'census_income_transform.py'

Writing census_income_transform.py
transform = Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_census_income_transform_module_file))
context.run(transform)
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
WARNING:absl:The default value of `force_tf_compat_v1` will change in a future release from `True` to `False`. Since this pipeline has TF 2 behaviors enabled, Transform will use native TF 2 at that point. You can test this behavior now by passing `force_tf_compat_v1=False` or disable it by explicitly setting `force_tf_compat_v1=True` in the Transform component.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/components/transform/executor.py:541: Schema (from tensorflow_transform.tf_metadata.dataset_schema) is deprecated and will be removed in a future version.
Instructions for updating:
Schema is a deprecated, use schema_utils.schema_from_feature_spec to create a `Schema`
INFO:absl:Loading /tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_transform.py because it has not been loaded before.
INFO:absl:/tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_transform.py is already loaded.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:TFT beam APIs accept both the TFXIO format and the instance dict format now. There is no need to set use_tfxio any more and it will be removed soon.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/259914d385c64e718981569626d0274c/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/5a8d2f32189a42faa1a8db83c514c74e/saved_model.pb
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/f4e3edcb37534b60889ae56fa82df09f/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/f4e3edcb37534b60889ae56fa82df09f/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

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

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_6:0\022/vocab_compute_and_apply_vocabulary_2_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_8:0\022/vocab_compute_and_apply_vocabulary_3_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_10:0\022/vocab_compute_and_apply_vocabulary_4_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_12:0\022/vocab_compute_and_apply_vocabulary_5_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_14:0\022/vocab_compute_and_apply_vocabulary_6_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_16:0\022/vocab_compute_and_apply_vocabulary_7_vocabulary"

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

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

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_6:0\022/vocab_compute_and_apply_vocabulary_2_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_8:0\022/vocab_compute_and_apply_vocabulary_3_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_10:0\022/vocab_compute_and_apply_vocabulary_4_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_12:0\022/vocab_compute_and_apply_vocabulary_5_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_14:0\022/vocab_compute_and_apply_vocabulary_6_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_16:0\022/vocab_compute_and_apply_vocabulary_7_vocabulary"

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

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

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_6:0\022/vocab_compute_and_apply_vocabulary_2_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_8:0\022/vocab_compute_and_apply_vocabulary_3_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_10:0\022/vocab_compute_and_apply_vocabulary_4_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_12:0\022/vocab_compute_and_apply_vocabulary_5_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_14:0\022/vocab_compute_and_apply_vocabulary_6_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_16:0\022/vocab_compute_and_apply_vocabulary_7_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized
transform.outputs
{'transform_graph': Channel(
    type_name: TransformGraph
    artifacts: [Artifact(artifact: id: 4
type_id: 11
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4"
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"
  }
}
state: LIVE
, artifact_type: id: 11
name: "TransformGraph"
)]
), 'transformed_examples': Channel(
    type_name: Examples
    artifacts: [Artifact(artifact: id: 5
type_id: 5
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transformed_examples/4"
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"
  }
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]
), 'updated_analyzer_cache': Channel(
    type_name: TransformCache
    artifacts: [Artifact(artifact: id: 6
type_id: 12
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/updated_analyzer_cache/4"
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"
  }
}
state: LIVE
, artifact_type: id: 12
name: "TransformCache"
)]
)}

Treinador

Vamos ver um exemplo de modelo de código definido pelo usuário abaixo (para uma introdução aos Keras APIs TensorFlow, veja o tutorial ):

_census_income_trainer_module_file = 'census_income_trainer.py'

Writing census_income_trainer.py
trainer = Trainer(
    module_file=os.path.abspath(_census_income_trainer_module_file),
    custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
    examples=transform.outputs['transformed_examples'],
    transform_graph=transform.outputs['transform_graph'],
    schema=schema_gen.outputs['schema'],
    train_args=trainer_pb2.TrainArgs(num_steps=100),
    eval_args=trainer_pb2.EvalArgs(num_steps=50))
context.run(trainer)
WARNING:absl:From <ipython-input-1-6ab3bbf2f5a0>:3: The name tfx.components.base.executor_spec.ExecutorClassSpec is deprecated. Please use tfx.dsl.components.base.executor_spec.ExecutorClassSpec instead.
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:Loading /tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_trainer.py because it has not been loaded before.
INFO:absl:Training model.
INFO:absl:Model: "functional_1"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:Age_xf (InputLayer)             [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Capital-Gain_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Capital-Loss_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Country_xf (InputLayer)         [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Education-Num_xf (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Education_xf (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Hours-per-week_xf (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Marital-Status_xf (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Occupation_xf (InputLayer)      [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Race_xf (InputLayer)            [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Relationship_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Sex_xf (InputLayer)             [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Workclass_xf (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures)  (None, 3)            0           Age_xf[0][0]                     
INFO:absl:                                                                 Capital-Gain_xf[0][0]            
INFO:absl:                                                                 Capital-Loss_xf[0][0]            
INFO:absl:                                                                 Country_xf[0][0]                 
INFO:absl:                                                                 Education-Num_xf[0][0]           
INFO:absl:                                                                 Education_xf[0][0]               
INFO:absl:                                                                 Hours-per-week_xf[0][0]          
INFO:absl:                                                                 Marital-Status_xf[0][0]          
INFO:absl:                                                                 Occupation_xf[0][0]              
INFO:absl:                                                                 Race_xf[0][0]                    
INFO:absl:                                                                 Relationship_xf[0][0]            
INFO:absl:                                                                 Sex_xf[0][0]                     
INFO:absl:                                                                 Workclass_xf[0][0]               
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 100)          400         dense_features[0][0]             
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 48)           3408        dense_1[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_3 (Dense)                 (None, 34)           1666        dense_2[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features_1 (DenseFeatures (None, 1710)         0           Age_xf[0][0]                     
INFO:absl:                                                                 Capital-Gain_xf[0][0]            
INFO:absl:                                                                 Capital-Loss_xf[0][0]            
INFO:absl:                                                                 Country_xf[0][0]                 
INFO:absl:                                                                 Education-Num_xf[0][0]           
INFO:absl:                                                                 Education_xf[0][0]               
INFO:absl:                                                                 Hours-per-week_xf[0][0]          
INFO:absl:                                                                 Marital-Status_xf[0][0]          
INFO:absl:                                                                 Occupation_xf[0][0]              
INFO:absl:                                                                 Race_xf[0][0]                    
INFO:absl:                                                                 Relationship_xf[0][0]            
INFO:absl:                                                                 Sex_xf[0][0]                     
INFO:absl:                                                                 Workclass_xf[0][0]               
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 1744)         0           dense_3[0][0]                    
INFO:absl:                                                                 dense_features_1[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_4 (Dense)                 (None, 1)            1745        concatenate[0][0]                
INFO:absl:==================================================================================================
INFO:absl:Total params: 14,289
INFO:absl:Trainable params: 14,289
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
1/100 [..............................] - ETA: 0s - loss: 0.7236 - binary_accuracy: 0.2250WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
Instructions for updating:
use `tf.profiler.experimental.stop` instead.
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0029s vs `on_train_batch_end` time: 0.0135s). Check your callbacks.
100/100 [==============================] - 1s 9ms/step - loss: 0.5104 - binary_accuracy: 0.7710 - val_loss: 0.4469 - val_binary_accuracy: 0.8005
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model/5/serving_model_dir/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model/5/serving_model_dir. ModelRun written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model_run/5
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

Avaliador

O Evaluator componente calcula métricas de desempenho modelo sobre o conjunto da avaliação. Ele usa o TensorFlow Modelo de Análise de biblioteca.

Evaluator terá como entrada a dados de ExampleGen , o modelo treinado desde Trainer e configuração de corte. A configuração de fatiamento permite fatiar suas métricas nos valores de recursos. Veja um exemplo dessa configuração abaixo:

from google.protobuf.wrappers_pb2 import BoolValue

eval_config = tfma.EvalConfig(
    model_specs=[
        # This assumes a serving model with signature 'serving_default'. If
        # using estimator based EvalSavedModel, add signature_name: 'eval' and 
        # remove the label_key.
        tfma.ModelSpec(label_key="Over-50K")
    ],
    metrics_specs=[
        tfma.MetricsSpec(
            # The metrics added here are in addition to those saved with the
            # model (assuming either a keras model or EvalSavedModel is used).
            # Any metrics added into the saved model (for example using
            # model.compile(..., metrics=[...]), etc) will be computed
            # automatically.
            # To add validation thresholds for metrics saved with the model,
            # add them keyed by metric name to the thresholds map.
            metrics=[
                tfma.MetricConfig(class_name='ExampleCount'),
                tfma.MetricConfig(class_name='BinaryAccuracy'),
                tfma.MetricConfig(class_name='FairnessIndicators',
                                  config='{ "thresholds": [0.5] }'),
            ]
        )
    ],
    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 by feature column Race and Sex.
        tfma.SlicingSpec(feature_keys=['Race']),
        tfma.SlicingSpec(feature_keys=['Sex']),
        tfma.SlicingSpec(feature_keys=['Race', 'Sex']),
    ],
    options = tfma.Options(compute_confidence_intervals=BoolValue(value=True))
)
# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.
evaluator = Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Evaluator
WARNING:absl:"maybe_add_baseline" and "maybe_remove_baseline" are deprecated,
        please use "has_baseline" instead.
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 {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}

INFO:absl:Using /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model/5/serving_model_dir as  model.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
  model_names: ""
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}

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 {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
  model_names: ""
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}

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 {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
  model_names: ""
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}
WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7f77a02d2510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Evaluator/evaluation/6.
INFO:absl:No threshold configured, will not validate model.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized
evaluator.outputs
{'evaluation': Channel(
    type_name: ModelEvaluation
    artifacts: [Artifact(artifact: id: 9
type_id: 17
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Evaluator/evaluation/6"
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"
  }
}
state: LIVE
, artifact_type: id: 17
name: "ModelEvaluation"
)]
), 'blessing': Channel(
    type_name: ModelBlessing
    artifacts: [Artifact(artifact: id: 10
type_id: 18
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Evaluator/blessing/6"
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"
  }
}
state: LIVE
, artifact_type: id: 18
name: "ModelBlessing"
)]
)}

Utilizando a evaluation de saída podemos mostrar a visualização padrão de métricas globais em todo o conjunto de avaliação.

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

Preencher as propriedades do ModelCard com o Model Card Toolkit

Agora que configuramos nosso pipeline TFX, usaremos o Model Card Toolkit para extrair os principais artefatos da execução e preencher um Model Card.

Conecte-se à loja MLMD usada pelo InteractiveContext

from ml_metadata.metadata_store import metadata_store
from IPython import display

mlmd_store = metadata_store.MetadataStore(context.metadata_connection_config)
model_uri = trainer.outputs["model"].get()[0].uri
INFO:absl:MetadataStore with DB connection initialized

Use Model Card Toolkit

Inicialize o Model Card Toolkit.

from model_card_toolkit import ModelCardToolkit

mct = ModelCardToolkit(mlmd_store=mlmd_store, model_uri=model_uri)

Crie a área de trabalho do Cartão de Modelo.

model_card = mct.scaffold_assets()

Anote mais informações no cartão de modelo.

Também é importante documentar as informações do modelo que podem ser importantes para os usuários de recebimento de dados, como suas limitações, casos de uso pretendidos, trocas e considerações éticas. Para cada uma dessas seções, podemos adicionar diretamente novos objetos JSON para representar essas informações.

model_card.model_details.name = 'Census Income Classifier'
model_card.model_details.overview = (
    'This is a wide and deep Keras model which aims to classify whether or not '
    'an individual has an income of over $50,000 based on various demographic '
    'features. The model is trained on the UCI Census Income Dataset. This is '
    'not a production model, and this dataset has traditionally only been used '
    'for research purposes. In this Model Card, you can review quantitative '
    'components of the model’s performance and data, as well as information '
    'about the model’s intended uses, limitations, and ethical considerations.'
)
model_card.model_details.owners = [
  {'name': 'Model Cards Team', 'contact': 'model-cards@google.com'}
]
model_card.considerations.use_cases = [
    'This dataset that this model was trained on was originally created to '
    'support the machine learning community in conducting empirical analysis '
    'of ML algorithms. The Adult Data Set can be used in fairness-related '
    'studies that compare inequalities across sex and race, based on '
    'people’s annual incomes.'
]
model_card.considerations.limitations = [
    'This is a class-imbalanced dataset across a variety of sensitive classes.'
    ' The ratio of male-to-female examples is about 2:1 and there are far more'
    ' examples with the “white” attribute than every other race combined. '
    'Furthermore, the ratio of $50,000 or less earners to $50,000 or more '
    'earners is just over 3:1. Due to the imbalance across income levels, we '
    'can see that our true negative rate seems quite high, while our true '
    'positive rate seems quite low. This is true to an even greater degree '
    'when we only look at the “female” sub-group, because there are even '
    'fewer female examples in the $50,000+ earner group, causing our model to '
    'overfit these examples. To avoid this, we can try various remediation '
    'strategies in future iterations (e.g. undersampling, hyperparameter '
    'tuning, etc), but we may not be able to fix all of the fairness issues.'
]
model_card.considerations.ethical_considerations = [{
    'name':
        'We risk expressing the viewpoint that the attributes in this dataset '
        'are the only ones that are predictive of someone’s income, even '
        'though we know this is not the case.',
    'mitigation_strategy':
        'As mentioned, some interventions may need to be performed to address '
        'the class imbalances in the dataset.'
}]

Filtrar e adicionar gráficos.

Podemos filtrar os gráficos gerados pelos componentes TFX para incluir aqueles mais relevantes para o Cartão de Modelo usando a função definida abaixo. Neste exemplo, nós filtrar por race e sex , dois atributos potencialmente sensíveis.

Cada cartão de modelo terá até três seções para gráficos - estatísticas de conjunto de dados de treinamento, estatísticas de conjunto de dados de avaliação e análise quantitativa do desempenho de nosso modelo.

# These are the graphs that will appear in the Quantiative Analysis portion of 
# the Model Card. Feel free to add or remove from this list. 
TARGET_EVAL_GRAPH_NAMES = [
  'fairness_indicators_metrics/false_positive_rate@0.5',
  'fairness_indicators_metrics/false_negative_rate@0.5',
  'binary_accuracy',
  'example_count | Race_X_Sex',
]

# These are the graphs that will appear in both the Train Set and Eval Set 
# portions of the Model Card. Feel free to add or remove from this list. 
TARGET_DATASET_GRAPH_NAMES = [
  'counts | Race',
  'counts | Sex',
]

def filter_graphs(graphics, target_graph_names):
  result = []
  for graph in graphics:
    for target_graph_name in target_graph_names:
      if graph.name.startswith(target_graph_name):
        result.append(graph)
  result.sort(key=lambda g: g.name)
  return result

# Populating the three different sections using the filter defined above. To 
# see all the graphs available in a section, we can iterate through each of the
# different collections. 
model_card.quantitative_analysis.graphics.collection = filter_graphs(
    model_card.quantitative_analysis.graphics.collection, TARGET_EVAL_GRAPH_NAMES)
model_card.model_parameters.data.eval.graphics.collection = filter_graphs(
    model_card.model_parameters.data.eval.graphics.collection, TARGET_DATASET_GRAPH_NAMES)
model_card.model_parameters.data.train.graphics.collection = filter_graphs(
    model_card.model_parameters.data.train.graphics.collection, TARGET_DATASET_GRAPH_NAMES)

Em seguida, adicionamos descrições (opcionais) para cada uma das seções do gráfico.

model_card.model_parameters.data.train.graphics.description = (
    'This section includes graphs displaying the class distribution for the '
    '“Race” and “Sex” attributes in our training dataset. We chose to '
    'show these graphs in particular because we felt it was important that '
    'users see the class imbalance.'
)
model_card.model_parameters.data.eval.graphics.description = (
    'Like the training set, we provide graphs showing the class distribution '
    'of the data we used to evaluate our model’s performance. '
)
model_card.quantitative_analysis.graphics.description = (
    'These graphs show how the model performs for data sliced by “Race”, '
    '“Sex” and the intersection of these attributes. The metrics we chose '
    'to display are “Accuracy”, “False Positive Rate”, and “False '
    'Negative Rate”, because we anticipated that the class imbalances might '
    'cause our model to underperform for certain groups.'
)
mct.update_model_card_json(model_card)

Gere o cartão de modelo.

Agora podemos exibir o cartão de modelo em formato HTML.

html = mct.export_format()
display.display(display.HTML(html))