Простое руководство по TFX Pipeline с использованием набора данных Penguin

Оптимизируйте свои подборки Сохраняйте и классифицируйте контент в соответствии со своими настройками.

Краткое руководство по запуску простого конвейера TFX.

В этом руководстве на основе записной книжки мы создадим и запустим конвейер TFX для простой модели классификации. Конвейер будет состоять из трех основных компонентов TFX: ExampleGen, Trainer и Pusher. Конвейер включает в себя самый минимальный рабочий процесс машинного обучения, такой как импорт данных, обучение модели и экспорт обученной модели.

См Понимание TFX трубопроводов более узнать о различных концепциях в TFX.

Настраивать

Сначала нам нужно установить пакет TFX Python и загрузить набор данных, который мы будем использовать для нашей модели.

Обновить Pip

Чтобы избежать обновления Pip в системе при локальном запуске, убедитесь, что мы работаем в Colab. Конечно, локальные системы можно модернизировать отдельно.

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

Установить TFX

pip install -U tfx

Вы перезапускали среду выполнения?

Если вы используете Google Colab, при первом запуске указанной выше ячейки вы должны перезапустить среду выполнения, нажав кнопку «ПЕРЕЗАГРУЗИТЬ ВРЕМЯ» выше или используя меню «Время выполнения> Перезапустить среду выполнения ...». Это связано с тем, как Colab загружает пакеты.

Проверьте версии TensorFlow и TFX.

import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.6.2
TFX version: 1.4.0

Настроить переменные

Для определения конвейера используются некоторые переменные. Вы можете настроить эти переменные по своему усмотрению. По умолчанию весь вывод из конвейера будет генерироваться в текущем каталоге.

import os

PIPELINE_NAME = "penguin-simple"

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

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

Подготовить пример данных

Мы загрузим пример набора данных для использования в нашем конвейере TFX. Набор данных мы используем это Palmer Пингвины набор данных , который также используется в других примерах TFX .

В этом наборе данных есть четыре числовых объекта:

  • culmen_length_mm
  • culmen_depth_mm
  • flipper_length_mm
  • body_mass_g

Все функции уже были нормализованы, чтобы иметь диапазон [0,1]. Мы будем строить модель классификации , которая предсказывает species пингвинов.

Поскольку TFX ExampleGen считывает входные данные из каталога, нам нужно создать каталог и скопировать в него набор данных.

import urllib.request
import tempfile

DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data')  # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_url, _data_filepath)
('/tmp/tfx-dataijanq9u3/data.csv', <http.client.HTTPMessage at 0x7f487953d110>)

Взгляните на файл CSV.

head {_data_filepath}
species,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g
0,0.2545454545454545,0.6666666666666666,0.15254237288135594,0.2916666666666667
0,0.26909090909090905,0.5119047619047618,0.23728813559322035,0.3055555555555556
0,0.29818181818181805,0.5833333333333334,0.3898305084745763,0.1527777777777778
0,0.16727272727272732,0.7380952380952381,0.3559322033898305,0.20833333333333334
0,0.26181818181818167,0.892857142857143,0.3050847457627119,0.2638888888888889
0,0.24727272727272717,0.5595238095238096,0.15254237288135594,0.2569444444444444
0,0.25818181818181823,0.773809523809524,0.3898305084745763,0.5486111111111112
0,0.32727272727272727,0.5357142857142859,0.1694915254237288,0.1388888888888889
0,0.23636363636363636,0.9642857142857142,0.3220338983050847,0.3055555555555556

Вы должны увидеть пять значений. species является одним из 0, 1 или 2, а все остальные функции должны иметь значения от 0 до 1.

Создать конвейер

Конвейеры TFX определяются с помощью API Python. Мы определим конвейер, который состоит из следующих трех компонентов.

  • CsvExampleGen: считывает файлы данных и преобразует их во внутренний формат TFX для дальнейшей обработки. Есть несколько ExampleGen s для различных форматов. В этом руководстве мы будем использовать CsvExampleGen, который принимает входной файл CSV.
  • Тренер: Обучает модель машинного обучения. Тренер компонент требует определения кода модели от пользователей. Вы можете использовать TensorFlow API , чтобы определить , как подготовить модель и сохранить его в формате _SAVED модели.
  • Pusher: копирует обученную модель за пределы конвейера TFX. Толкатель компонент можно рассматривать развертывание процесса обучаемой модели ML.

Прежде чем фактически определять конвейер, нам нужно сначала написать код модели для компонента Trainer.

Написать код обучения модели

Мы создадим простую модель DNN для классификации с помощью TensorFlow Keras API. Этот обучающий код модели будет сохранен в отдельный файл.

В этом уроке мы будем использовать Generic Trainer из TFX , которые поддерживают модели Keras на основе. Вам нужно написать файл Python , содержащий run_fn функцию, которая является точкой входа для Trainer компонента.

_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}

from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils

from tfx import v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_metadata.proto.v0 import schema_pb2

_FEATURE_KEYS = [
    'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'

_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10

# Since we're not generating or creating a schema, we will instead create
# a feature spec.  Since there are a fairly small number of features this is
# manageable for this dataset.
_FEATURE_SPEC = {
    **{
        feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)
           for feature in _FEATURE_KEYS
       },
    _LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)
}


def _input_fn(file_pattern: List[str],
              data_accessor: tfx.components.DataAccessor,
              schema: schema_pb2.Schema,
              batch_size: int = 200) -> tf.data.Dataset:
  """Generates features and label for training.

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

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


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

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

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

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


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

  Args:
    fn_args: Holds args used to train the model as name/value pairs.
  """

  # This schema is usually either an output of SchemaGen or a manually-curated
  # version provided by pipeline author. A schema can also derived from TFT
  # graph if a Transform component is used. In the case when either is missing,
  # `schema_from_feature_spec` could be used to generate schema from very simple
  # feature_spec, but the schema returned would be very primitive.
  schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)

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

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

  # The result of the training should be saved in `fn_args.serving_model_dir`
  # directory.
  model.save(fn_args.serving_model_dir, save_format='tf')
Writing penguin_trainer.py

Теперь вы выполнили все подготовительные шаги для создания конвейера TFX.

Напишите определение конвейера

Мы определяем функцию для создания конвейера TFX. Pipeline объект представляет собой TFX трубопровод , который может быть запущен с помощью одного из трубопроводов систем оркестровки что TFX опор.

def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
                     module_file: str, serving_model_dir: str,
                     metadata_path: str) -> tfx.dsl.Pipeline:
  """Creates a three component penguin pipeline with TFX."""
  # Brings data into the pipeline.
  example_gen = tfx.components.CsvExampleGen(input_base=data_root)

  # Uses user-provided Python function that trains a model.
  trainer = tfx.components.Trainer(
      module_file=module_file,
      examples=example_gen.outputs['examples'],
      train_args=tfx.proto.TrainArgs(num_steps=100),
      eval_args=tfx.proto.EvalArgs(num_steps=5))

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

  # Following three components will be included in the pipeline.
  components = [
      example_gen,
      trainer,
      pusher,
  ]

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

Запустите конвейер

TFX поддерживает несколько оркестраторов для запуска конвейеров. В этом уроке мы будем использовать LocalDagRunner , который входит в TFX Python пакетов и работает трубопроводов на местную окружающую среду. Мы часто называем конвейеры TFX «DAG», что означает ориентированный ациклический граф.

LocalDagRunner обеспечивает быстрые итерации для developemnt и отладки. TFX также поддерживает другие оркестраторы, включая Kubeflow Pipelines и Apache Airflow, которые подходят для производственных сценариев.

См TFX на Cloud Platform AI Трубопроводы или TFX Airflow Учебник более узнать о других системах оркестровки.

Теперь мы создаем LocalDagRunner и передать Pipeline объект , созданный из функции мы уже определили.

Конвейер работает напрямую, и вы можете видеть журналы хода конвейера, включая обучение модели машинного обучения.

tfx.orchestration.LocalDagRunner().run(
  _create_pipeline(
      pipeline_name=PIPELINE_NAME,
      pipeline_root=PIPELINE_ROOT,
      data_root=DATA_ROOT,
      module_file=_trainer_module_file,
      serving_model_dir=SERVING_MODEL_DIR,
      metadata_path=METADATA_PATH))
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_trainer.py' (including modules: ['penguin_trainer']).
INFO:absl:User module package has hash fingerprint version a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp28n_co8j/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpfb02sbta', '--dist-dir', '/tmp/tmpyu7gi15_']
/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 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl'
INFO:absl:Using deployment config:
 executor_specs {
  key: "CsvExampleGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "Pusher"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.pusher.executor.Executor"
    }
  }
}
executor_specs {
  key: "Trainer"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.trainer.executor.GenericExecutor"
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  sqlite {
    filename_uri: "metadata/penguin-simple/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

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

INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-simple"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:44:06.706974"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-simple.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-dataijanq9u3"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
  parameters {
    key: "output_file_format"
    value {
      field_value {
        int_value: 5
      }
    }
  }
}
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_trainer.py -> build/lib
installing to /tmp/tmpfb02sbta
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmpfb02sbta
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/tmpfb02sbta/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3.7.egg-info
running install_scripts
creating /tmp/tmpfb02sbta/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/WHEEL
creating '/tmp/tmpyu7gi15_/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl' and adding '/tmp/tmpfb02sbta' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/RECORD'
removing /tmp/tmpfb02sbta
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 10:44:07.061197 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:44:07.067816 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:44:07.074599 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:44:07.081624 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
I1205 10:44:07.136307 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-simple/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638701046,sum_checksum:1638701046"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
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execution_options {
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, pipeline_info=id: "penguin-simple"
, pipeline_run_id='2021-12-05T10:44:06.706974')
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-dataijanq9u3/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-simple/CsvExampleGen/examples/1"
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I1205 10:44:08.274386 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 2
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=2, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-simple/CsvExampleGen/examples/1"
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custom_properties {
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custom_properties {
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create_time_since_epoch: 1638701048257
last_update_time_since_epoch: 1638701048257
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inputs {
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upstream_nodes: "CsvExampleGen"
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execution_options {
  caching_options {
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, pipeline_info=id: "penguin-simple"
, pipeline_run_id='2021-12-05T10:44:06.706974')
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:udf_utils.get_fn {'custom_config': 'null', 'module_path': 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl', 'train_args': '{\n  "num_steps": 100\n}', 'eval_args': '{\n  "num_steps": 5\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp9yk6w_js', 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl']
Processing ./pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:culmen_length_mm (InputLayer)   [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer)    [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer)  [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:body_mass_g (InputLayer)        [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 4)            0           culmen_length_mm[0][0]           
INFO:absl:                                                                 culmen_depth_mm[0][0]            
INFO:absl:                                                                 flipper_length_mm[0][0]          
INFO:absl:                                                                 body_mass_g[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 8)            40          concatenate[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 3)            27          dense_1[0][0]                    
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 1s 3ms/step - loss: 0.4074 - sparse_categorical_accuracy: 0.8755 - val_loss: 0.0760 - val_sparse_categorical_accuracy: 0.9800
2021-12-05 10:44:13.263941: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: pipelines/penguin-simple/Trainer/model/2/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-simple/Trainer/model/2/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-simple/Trainer/model/2/Format-Serving. ModelRun written to pipelines/penguin-simple/Trainer/model_run/2
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 2 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model/2"
custom_properties {
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custom_properties {
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, artifact_type: name: "ModelRun"
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Trainer is finished.
I1205 10:44:13.795414 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is running.
I1205 10:44:13.799805 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running launcher for node_info {
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    name: "tfx.components.pusher.component.Pusher"
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  id: "Pusher"
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contexts {
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          }
        }
        output_key: "model"
      }
    }
  }
}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
        type {
          name: "PushedModel"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "custom_config"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
  parameters {
    key: "push_destination"
    value {
      field_value {
        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-simple\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 10:44:13.821346 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'model': [Artifact(artifact: id: 2
type_id: 17
uri: "pipelines/penguin-simple/Trainer/model/2"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638701053803
last_update_time_since_epoch: 1638701053803
, artifact_type: id: 17
name: "Model"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-simple/Pusher/pushed_model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:Pusher:pushed_model:0"
  }
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-simple"\n  }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-simple/Pusher/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-simple/Pusher/.system/stateful_working_dir/2021-12-05T10:44:06.706974', tmp_dir='pipelines/penguin-simple/Pusher/.system/executor_execution/3/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.pusher.component.Pusher"
  }
  id: "Pusher"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-simple"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:44:06.706974"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-simple.Pusher"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        producer_node_query {
          id: "Trainer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-simple"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T10:44:06.706974"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-simple.Trainer"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
        output_key: "model"
      }
    }
  }
}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
        type {
          name: "PushedModel"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "custom_config"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
  parameters {
    key: "push_destination"
    value {
      field_value {
        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-simple\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-simple"
, pipeline_run_id='2021-12-05T10:44:06.706974')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1638701053
INFO:absl:Model written to serving path serving_model/penguin-simple/1638701053.
INFO:absl:Model pushed to pipelines/penguin-simple/Pusher/pushed_model/3.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-simple/Pusher/pushed_model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:Pusher:pushed_model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "PushedModel"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Pusher is finished.
I1205 10:44:13.851651 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type

Вы должны увидеть «INFO: absl: Component Pusher is finished». в конце журналов, если конвейер завершился успешно. Поскольку Pusher компонент является последним компонентом трубопровода.

Толкатель компонент толкает обученную модель на SERVING_MODEL_DIR которая является serving_model/penguin-simple каталога , если вы не изменили переменные в предыдущих шагах. Вы можете увидеть результат в браузере файлов на левой панели в Colab или с помощью следующей команды:

# List files in created model directory.
find {SERVING_MODEL_DIR}
serving_model/penguin-simple
serving_model/penguin-simple/1638701053
serving_model/penguin-simple/1638701053/keras_metadata.pb
serving_model/penguin-simple/1638701053/assets
serving_model/penguin-simple/1638701053/variables
serving_model/penguin-simple/1638701053/variables/variables.data-00000-of-00001
serving_model/penguin-simple/1638701053/variables/variables.index
serving_model/penguin-simple/1638701053/saved_model.pb

Следующие шаги

Вы можете найти больше ресурсов на https://www.tensorflow.org/tfx/tutorials

См Понимание TFX трубопроводов более узнать о различных концепциях в TFX.