MLコミュニティデーは11月9日です! TensorFlow、JAXからの更新のために私たちに参加し、より多くの詳細をご覧ください

TFXパイプラインとTensorFlow変換を使用した機能エンジニアリング

入力データを変換し、TFXパイプラインを使用してモデルをトレーニングします。

このノートブックベースのチュートリアルでは、TFXパイプラインを作成して実行し、生の入力データを取り込んで、MLトレーニング用に適切に前処理します。このノートブックは、私たちが組み込まTFXパイプラインに基づいておりTFXパイプラインとTensorFlowデータ検証のチュートリアルを使用したデータの検証。まだ読んでいない場合は、このノートブックに進む前に読んでください。

特徴エンジニアリングを使用すると、データの予測品質を向上させたり、次元を削減したりできます。 TFXを使用する利点の1つは、変換コードを1回記述するだけで、トレーニングとサービングの偏りを回避するために、結果の変換がトレーニングとサービングの間で一貫することです。

私たちは、追加するTransformパイプラインに部品。変換コンポーネントを使用して実装されてtf.transformのライブラリを。

参照してくださいTFXパイプラインが理解TFXの様々な概念についての詳細を学ぶために。

設定

まず、TFX Pythonパッケージをインストールし、モデルに使用するデータセットをダウンロードする必要があります。

アップグレードピップ

ローカルで実行しているときにシステムでPipをアップグレードしないようにするには、Colabで実行していることを確認してください。もちろん、ローカルシステムは個別にアップグレードできます。

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

TFXをインストールする

pip install -U tfx

ランタイムを再起動しましたか?

上記のセルを初めて実行するときにGoogleColabを使用している場合は、[ランタイムの再起動]ボタンをクリックするか、[ランタイム]> [ランタイムの再起動...]メニューを使用してランタイムを再起動する必要があります。これは、Colabがパッケージをロードする方法が原因です。

TensorFlowとTFXのバージョンを確認してください。

import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.5.1
TFX version: 1.2.0

変数を設定する

パイプラインを定義するために使用されるいくつかの変数があります。これらの変数は必要に応じてカスタマイズできます。デフォルトでは、パイプラインからのすべての出力は現在のディレクトリの下に生成されます。

import os

PIPELINE_NAME = "penguin-transform"

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

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

サンプルデータを準備する

TFXパイプラインで使用するサンプルデータセットをダウンロードします。私たちが使用しているデータセットがあるパーマーペンギンセット

しかし、すでに前処理データセットを使用し、前のチュートリアルとは異なり、我々はパーマーペンギンのデータセットを使用します。

TFX ExampleGenコンポーネントはディレクトリから入力を読み取るため、ディレクトリを作成してデータセットをそこにコピーする必要があります。

import urllib.request
import tempfile

DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data')  # Create a temporary directory.
_data_path = 'https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins_size.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_path, _data_filepath)
('/tmp/tfx-data3sjvggnf/data.csv', <http.client.HTTPMessage at 0x7f419aea05d0>)

生データがどのように見えるかを簡単に見てみましょう。

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

表現されている欠損値を持ついくつかのエントリがありNA 。このチュートリアルでは、これらのエントリを削除します。

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

あなたはペンギンを説明する7つの特徴を見ることができるはずです。前のチュートリアルと同じ機能セット(「culmen_length_mm」、「culmen_depth_mm」、「flipper_length_mm」、「body_mass_g」)を使用し、ペンギンの「種」を予測します。

唯一の違いは、入力データが前処理されていないことです。このチュートリアルでは、「島」や「性別」などの他の機能は使用しないことに注意してください。

スキーマファイルを準備する

で説明したようにTFXパイプラインとTensorFlowデータ検証のチュートリアルを使用したデータの検証、我々は、データセットのスキーマファイルを必要とします。データセットは前のチュートリアルとは異なるため、再度生成する必要があります。このチュートリアルでは、これらの手順をスキップして、準備されたスキーマファイルを使用します。

import shutil

SCHEMA_PATH = 'schema'

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

os.makedirs(SCHEMA_PATH, exist_ok=True)
urllib.request.urlretrieve(_schema_uri, _schema_filepath)
('schema/schema.pbtxt', <http.client.HTTPMessage at 0x7f419aeb0490>)

このスキーマファイルは、手動で変更することなく、前のチュートリアルと同じパイプラインで作成されました。

パイプラインを作成する

TFXパイプラインは、PythonAPIを使用して定義されます。私たちは、追加されますTransform私たちが作成したパイプラインにコンポーネントをデータ検証のチュートリアル

Aは、成分を変換からの入力データを必要とExampleGen成分とからスキーマSchemaGen成分、及び「グラフ変換」を生成します。出力はで使用されるTrainerコンポーネント。 Transformは、オプションで「変換されたデータ」を生成できます。これは、変換後のマテリアライズされたデータです。ただし、このチュートリアルでは、中間変換データを具体化せずに、トレーニング中にデータを変換します。

もう一つ注意すべきは、我々は、Pythonの関数を定義する必要があるということですpreprocessing_fn変換する方法を入力データを説明するために。これは、モデル定義用のユーザーコードも必要とするTrainerコンポーネントに似ています。

前処理とトレーニングコードを書く

2つのPython関数を定義する必要があります。 1つは変換用、もう1つはトレーナー用です。

preprocessing_fn

変換コンポーネントは、指定された関数を見つけるでしょうpreprocessing_fn我々が行ったように与えられたモジュールファイルにTrainerコンポーネント。また、使用して、特定の関数を指定することができますpreprocessing_fnパラメータ変換コンポーネントのを。

この例では、2種類の変換を行います。以下のような連続する数値の機能についてculmen_length_mmbody_mass_g 、我々は使用して、これらの値を正規化しますtft.scale_to_z_scoreの機能を。ラベル機能の場合、文字列ラベルを数値のインデックス値に変換する必要があります。私たちは、使用するtf.lookup.StaticHashTable変換するために。

簡単に変換フィールドを識別するために、我々は追加_xf変換機能名に接尾辞。

run_fn

モデル自体は前のチュートリアルとほぼ同じですが、今回はTransformコンポーネントの変換グラフを使用して入力データを変換します。

前のチュートリアルとのもう1つの重要な違いは、モデルの計算グラフだけでなく、変換コンポーネントで生成される前処理の変換グラフも含む、サービング用のモデルをエクスポートすることです。着信要求を処理するために使用される別の関数を定義する必要があります。あなたは、同じ機能のことを見ることができます_apply_preprocessingトレーニングデータとサービス提供要求の両方のために使用されました。

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


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

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

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

_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10


# NEW: Transformed features will have '_xf' suffix.
def _transformed_name(key):
  return key + '_xf'


# NEW: TFX Transform will call this function.
def preprocessing_fn(inputs):
  """tf.transform's callback function for preprocessing inputs.

  Args:
    inputs: map from feature keys to raw not-yet-transformed features.

  Returns:
    Map from string feature key to transformed feature.
  """
  outputs = {}

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

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

  return outputs


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


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

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

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

  return serve_tf_examples_fn


def _input_fn(file_pattern: List[Text],
              data_accessor: tfx.components.DataAccessor,
              tf_transform_output: tft.TFTransformOutput,
              batch_size: int = 200) -> tf.data.Dataset:
  """Generates features and label for tuning/training.

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

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

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

  return dataset.map(apply_transform).repeat()


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

  Returns:
    A Keras Model.
  """
  # The model below is built with Functional API, please refer to
  # https://www.tensorflow.org/guide/keras/overview for all API options.
  inputs = [
      keras.layers.Input(shape=(1,), name=_transformed_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.
  """
  tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)

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

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

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

これで、TFXパイプラインを構築するためのすべての準備手順が完了しました。

パイプライン定義を書く

TFXパイプラインを作成する関数を定義します。 A Pipelineオブジェクトは、TFXのサポートされているパイプライン・オーケストレーションシステムのいずれかを使用して実行することができるTFXパイプラインを表します。

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

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

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

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

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

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

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

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

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

  components = [
      example_gen,
      statistics_gen,
      schema_importer,
      example_validator,

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

      trainer,
      pusher,
  ]

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

パイプラインを実行する

私たちは、使用するLocalDagRunner前のチュートリアルのように。

tfx.orchestration.LocalDagRunner().run(
  _create_pipeline(
      pipeline_name=PIPELINE_NAME,
      pipeline_root=PIPELINE_ROOT,
      data_root=DATA_ROOT,
      schema_path=SCHEMA_PATH,
      module_file=_module_file,
      serving_model_dir=SERVING_MODEL_DIR,
      metadata_path=METADATA_PATH))
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_utils.py' (including modules: ['penguin_utils']).
INFO:absl:User module package has hash fingerprint version 031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpiuxk_9im/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmppc1sy1e7', '--dist-dir', '/tmp/tmpupk4quit']
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'; target user module is 'penguin_utils'.
INFO:absl:Full user module path is 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_utils.py' (including modules: ['penguin_utils']).
INFO:absl:User module package has hash fingerprint version 031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp4y3kvnga/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpzw6aqado', '--dist-dir', '/tmp/tmpnjua9354']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_utils.py -> build/lib
installing to /tmp/tmppc1sy1e7
running install
running install_lib
copying build/lib/penguin_utils.py -> /tmp/tmppc1sy1e7
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmppc1sy1e7/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3.7.egg-info
running install_scripts
creating /tmp/tmppc1sy1e7/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/WHEEL
creating '/tmp/tmpupk4quit/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl' and adding '/tmp/tmppc1sy1e7' to it
adding 'penguin_utils.py'
adding 'tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/RECORD'
removing /tmp/tmppc1sy1e7
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'; target user module is 'penguin_utils'.
INFO:absl:Full user module path is 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'
INFO:absl:Running pipeline:
 pipeline_info {
  id: "penguin-transform"
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
      }
      id: "CsvExampleGen"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-transform"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-09-30T02:21:21.719173"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-transform.CsvExampleGen"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "examples"
        value {
          artifact_spec {
            type {
              name: "Examples"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
              properties {
                key: "version"
                value: INT
              }
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "input_base"
        value {
          field_value {
            string_value: "/tmp/tfx-data3sjvggnf"
          }
        }
      }
      parameters {
        key: "input_config"
        value {
          field_value {
            string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
          }
        }
      }
      parameters {
        key: "output_config"
        value {
          field_value {
            string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
          }
        }
      }
      parameters {
        key: "output_data_format"
        value {
          field_value {
            int_value: 6
          }
        }
      }
      parameters {
        key: "output_file_format"
        value {
          field_value {
            int_value: 5
          }
        }
      }
    }
    downstream_nodes: "StatisticsGen"
    downstream_nodes: "Trainer"
    downstream_nodes: "Transform"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.dsl.components.common.importer.Importer"
      }
      id: "schema_importer"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-transform"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-09-30T02:21:21.719173"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-transform.schema_importer"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "result"
        value {
          artifact_spec {
            type {
              name: "Schema"
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "artifact_uri"
        value {
          field_value {
            string_value: "schema"
          }
        }
      }
      parameters {
        key: "reimport"
        value {
          field_value {
            int_value: 0
          }
        }
      }
    }
    downstream_nodes: "ExampleValidator"
    downstream_nodes: "Transform"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.statistics_gen.component.StatisticsGen"
      }
      id: "StatisticsGen"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
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        name {
          field_value {
            string_value: "penguin-transform"
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      contexts {
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        name {
          field_value {
            string_value: "2021-09-30T02:21:21.719173"
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      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-transform.StatisticsGen"
          }
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    }
    inputs {
      inputs {
        key: "examples"
        value {
          channels {
            producer_node_query {
              id: "CsvExampleGen"
            }
            context_queries {
              type {
                name: "pipeline"
              }
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                  string_value: "penguin-transform"
                }
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            context_queries {
              type {
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              }
              name {
                field_value {
                  string_value: "2021-09-30T02:21:21.719173"
                }
              }
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            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-transform.CsvExampleGen"
                }
              }
            }
            artifact_query {
              type {
                name: "Examples"
              }
            }
            output_key: "examples"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "statistics"
        value {
          artifact_spec {
            type {
              name: "ExampleStatistics"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "exclude_splits"
        value {
          field_value {
            string_value: "[]"
          }
        }
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    }
    upstream_nodes: "CsvExampleGen"
    downstream_nodes: "ExampleValidator"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
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      type {
        name: "tfx.components.transform.component.Transform"
      }
      id: "Transform"
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    contexts {
      contexts {
        type {
          name: "pipeline"
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        name {
          field_value {
            string_value: "penguin-transform"
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        }
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      contexts {
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          name: "pipeline_run"
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        name {
          field_value {
            string_value: "2021-09-30T02:21:21.719173"
          }
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      }
      contexts {
        type {
          name: "node"
        }
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            string_value: "penguin-transform.Transform"
          }
        }
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    }
    inputs {
      inputs {
        key: "examples"
        value {
          channels {
            producer_node_query {
              id: "CsvExampleGen"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-transform"
                }
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              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-09-30T02:21:21.719173"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-transform.CsvExampleGen"
                }
              }
            }
            artifact_query {
              type {
                name: "Examples"
              }
            }
            output_key: "examples"
          }
        }
      }
      inputs {
        key: "schema"
        value {
          channels {
            producer_node_query {
              id: "schema_importer"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-transform"
                }
              }
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            context_queries {
              type {
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              name {
                field_value {
                  string_value: "2021-09-30T02:21:21.719173"
                }
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            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-transform.schema_importer"
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              }
            }
            artifact_query {
              type {
                name: "Schema"
              }
            }
            output_key: "result"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "post_transform_anomalies"
        value {
          artifact_spec {
            type {
              name: "ExampleAnomalies"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
            }
          }
        }
      }
      outputs {
        key: "post_transform_schema"
        value {
          artifact_spec {
            type {
              name: "Schema"
            }
          }
        }
      }
      outputs {
        key: "post_transform_stats"
        value {
          artifact_spec {
            type {
              name: "ExampleStatistics"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
            }
          }
        }
      }
      outputs {
        key: "pre_transform_schema"
        value {
          artifact_spec {
            type {
              name: "Schema"
            }
          }
        }
      }
      outputs {
        key: "pre_transform_stats"
        value {
          artifact_spec {
            type {
              name: "ExampleStatistics"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
            }
          }
        }
      }
      outputs {
        key: "transform_graph"
        value {
          artifact_spec {
            type {
              name: "TransformGraph"
            }
          }
        }
      }
      outputs {
        key: "updated_analyzer_cache"
        value {
          artifact_spec {
            type {
              name: "TransformCache"
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "custom_config"
        value {
          field_value {
            string_value: "null"
          }
        }
      }
      parameters {
        key: "disable_statistics"
        value {
          field_value {
            int_value: 0
          }
        }
      }
      parameters {
        key: "force_tf_compat_v1"
        value {
          field_value {
            int_value: 0
          }
        }
      }
      parameters {
        key: "module_path"
        value {
          field_value {
            string_value: "penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl"
          }
        }
      }
    }
    upstream_nodes: "CsvExampleGen"
    upstream_nodes: "schema_importer"
    downstream_nodes: "Trainer"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.example_validator.component.ExampleValidator"
      }
      id: "ExampleValidator"
    }
    contexts {
      contexts {
        type {
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        }
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      contexts {
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        }
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            string_value: "2021-09-30T02:21:21.719173"
          }
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      contexts {
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          field_value {
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    inputs {
      inputs {
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          channels {
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            context_queries {
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                field_value {
                  string_value: "2021-09-30T02:21:21.719173"
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            context_queries {
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            artifact_query {
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          }
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      }
      inputs {
        key: "statistics"
        value {
          channels {
            producer_node_query {
              id: "StatisticsGen"
            }
            context_queries {
              type {
                name: "pipeline"
              }
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                  string_value: "2021-09-30T02:21:21.719173"
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            context_queries {
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              }
              name {
                field_value {
                  string_value: "penguin-transform.StatisticsGen"
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            }
            artifact_query {
              type {
                name: "ExampleStatistics"
              }
            }
            output_key: "statistics"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "anomalies"
        value {
          artifact_spec {
            type {
              name: "ExampleAnomalies"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
            }
          }
        }
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    parameters {
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          }
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    upstream_nodes: "StatisticsGen"
    upstream_nodes: "schema_importer"
    execution_options {
      caching_options {
      }
    }
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}
nodes {
  pipeline_node {
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    }
    contexts {
      contexts {
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      inputs {
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            }
            artifact_query {
              type {
                name: "TransformGraph"
              }
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            output_key: "transform_graph"
          }
        }
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    }
    outputs {
      outputs {
        key: "model"
        value {
          artifact_spec {
            type {
              name: "Model"
            }
          }
        }
      }
      outputs {
        key: "model_run"
        value {
          artifact_spec {
            type {
              name: "ModelRun"
            }
          }
        }
      }
    }
    parameters {
      parameters {
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            string_value: "null"
          }
        }
      }
      parameters {
        key: "eval_args"
        value {
          field_value {
            string_value: "{\n  \"num_steps\": 5\n}"
          }
        }
      }
      parameters {
        key: "module_path"
        value {
          field_value {
            string_value: "penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl"
          }
        }
      }
      parameters {
        key: "train_args"
        value {
          field_value {
            string_value: "{\n  \"num_steps\": 100\n}"
          }
        }
      }
    }
    upstream_nodes: "CsvExampleGen"
    upstream_nodes: "Transform"
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    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
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          name: "pipeline"
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        name {
          field_value {
            string_value: "penguin-transform"
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      contexts {
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            string_value: "2021-09-30T02:21:21.719173"
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        }
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      contexts {
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          name: "node"
        }
        name {
          field_value {
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    }
    inputs {
      inputs {
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        value {
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            }
            context_queries {
              type {
                name: "pipeline"
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              name {
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              }
            }
            artifact_query {
              type {
                name: "Model"
              }
            }
            output_key: "model"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "pushed_model"
        value {
          artifact_spec {
            type {
              name: "PushedModel"
            }
          }
        }
      }
    }
    parameters {
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        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-transform\"\n  }\n}"
          }
        }
      }
    }
    upstream_nodes: "Trainer"
    execution_options {
      caching_options {
      }
    }
  }
}
runtime_spec {
  pipeline_root {
    field_value {
      string_value: "pipelines/penguin-transform"
    }
  }
  pipeline_run_id {
    field_value {
      string_value: "2021-09-30T02:21:21.719173"
    }
  }
}
execution_mode: SYNC
deployment_config {
  type_url: "type.googleapis.com/tfx.orchestration.IntermediateDeploymentConfig"
  value: "\n\236\001\n\rCsvExampleGen\022\214\001\nHtype.googleapis.com/tfx.orchestration.executable_spec.BeamExecutableSpec\022@\n>\n<tfx.components.example_gen.csv_example_gen.executor.Executor\n\206\001\n\006Pusher\022|\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\022)\n\'tfx.components.pusher.executor.Executor\n\207\001\n\tTransform\022z\nHtype.googleapis.com/tfx.orchestration.executable_spec.BeamExecutableSpec\022.\n,\n*tfx.components.transform.executor.Executor\n\234\001\n\020ExampleValidator\022\207\001\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\0224\n2tfx.components.example_validator.executor.Executor\n\220\001\n\007Trainer\022\204\001\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\0221\n/tfx.components.trainer.executor.GenericExecutor\n\220\001\n\rStatisticsGen\022\177\nHtype.googleapis.com/tfx.orchestration.executable_spec.BeamExecutableSpec\0223\n1\n/tfx.components.statistics_gen.executor.Executor\022\230\001\n\rCsvExampleGen\022\206\001\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\0223\n1tfx.components.example_gen.driver.FileBasedDriver*`\n0type.googleapis.com/ml_metadata.ConnectionConfig\022,\032*\n&metadata/penguin-transform/metadata.db\020\003"
}

INFO:absl:Using deployment config:
 executor_specs {
  key: "CsvExampleGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "ExampleValidator"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_validator.executor.Executor"
    }
  }
}
executor_specs {
  key: "Pusher"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.pusher.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "Trainer"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.trainer.executor.GenericExecutor"
    }
  }
}
executor_specs {
  key: "Transform"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.transform.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  sqlite {
    filename_uri: "metadata/penguin-transform/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

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

INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-09-30T02:21:21.719173"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-data3sjvggnf"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
  parameters {
    key: "output_file_format"
    value {
      field_value {
        int_value: 5
      }
    }
  }
}
downstream_nodes: "StatisticsGen"
downstream_nodes: "Trainer"
downstream_nodes: "Transform"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0930 02:21:21.740916 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I0930 02:21:21.747733 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I0930 02:21:21.754034 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I0930 02:21:21.760943 23080 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
I0930 02:21:21.775208 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 1
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1632968480,sum_checksum:1632968480"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-09-30T02:21:21.719173:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}), exec_properties={'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'input_base': '/tmp/tfx-data3sjvggnf', 'output_config': '{\n  "split_config": {\n    "splits": [\n      {\n        "hash_buckets": 2,\n        "name": "train"\n      },\n      {\n        "hash_buckets": 1,\n        "name": "eval"\n      }\n    ]\n  }\n}', 'output_data_format': 6, 'output_file_format': 5, 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:13161,xor_checksum:1632968480,sum_checksum:1632968480'}, execution_output_uri='pipelines/penguin-transform/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/CsvExampleGen/.system/stateful_working_dir/2021-09-30T02:21:21.719173', tmp_dir='pipelines/penguin-transform/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-09-30T02:21:21.719173"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-data3sjvggnf"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
  parameters {
    key: "output_file_format"
    value {
      field_value {
        int_value: 5
      }
    }
  }
}
downstream_nodes: "StatisticsGen"
downstream_nodes: "Trainer"
downstream_nodes: "Transform"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-09-30T02:21:21.719173')
INFO:absl:Generating examples.
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_utils.py -> build/lib
installing to /tmp/tmpzw6aqado
running install
running install_lib
copying build/lib/penguin_utils.py -> /tmp/tmpzw6aqado
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/tmpzw6aqado/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3.7.egg-info
running install_scripts
creating /tmp/tmpzw6aqado/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/WHEEL
creating '/tmp/tmpnjua9354/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl' and adding '/tmp/tmpzw6aqado' to it
adding 'penguin_utils.py'
adding 'tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403.dist-info/RECORD'
removing /tmp/tmpzw6aqado
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-data3sjvggnf/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1632968480,sum_checksum:1632968480"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-09-30T02:21:21.719173:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.2.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component schema_importer is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.dsl.components.common.importer.Importer"
  }
  id: "schema_importer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-09-30T02:21:21.719173"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.schema_importer"
      }
    }
  }
}
outputs {
  outputs {
    key: "result"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "artifact_uri"
    value {
      field_value {
        string_value: "schema"
      }
    }
  }
  parameters {
    key: "reimport"
    value {
      field_value {
        int_value: 0
      }
    }
  }
}
downstream_nodes: "ExampleValidator"
downstream_nodes: "Transform"
execution_options {
  caching_options {
  }
}

INFO:absl:Running as an importer node.
INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:22.931212 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Processing source uri: schema, properties: {}, custom_properties: {}
INFO:absl:Component schema_importer is finished.
I0930 02:21:22.940133 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-09-30T02:21:21.719173"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-transform"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-09-30T02:21:21.719173"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:22.958328 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1632968480,sum_checksum:1632968480"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-09-30T02:21:21.719173:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.2.0"
  }
}
state: LIVE
create_time_since_epoch: 1632968482916
last_update_time_since_epoch: 1632968482916
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-transform/StatisticsGen/statistics/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-09-30T02:21:21.719173:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-transform/StatisticsGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/StatisticsGen/.system/stateful_working_dir/2021-09-30T02:21:21.719173', tmp_dir='pipelines/penguin-transform/StatisticsGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-09-30T02:21:21.719173"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-transform"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-09-30T02:21:21.719173"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
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upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-09-30T02:21:21.719173')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to pipelines/penguin-transform/StatisticsGen/statistics/3/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to pipelines/penguin-transform/StatisticsGen/statistics/3/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-transform/StatisticsGen/statistics/3"
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, artifact_type: name: "ExampleStatistics"
properties {
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properties {
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)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component Transform is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.transform.component.Transform"
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  id: "Transform"
}
contexts {
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parameters {
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}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Trainer"
execution_options {
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}

INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:24.990144 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 4
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
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custom_properties {
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custom_properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1632968482916
last_update_time_since_epoch: 1632968482916
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properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1632968482943
last_update_time_since_epoch: 1632968482943
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properties {
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contexts {
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inputs {
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outputs {
  outputs {
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    value {
      artifact_spec {
        type {
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            value: INT
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          properties {
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  outputs {
    key: "post_transform_schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
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  outputs {
    key: "post_transform_stats"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
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  outputs {
    key: "pre_transform_schema"
    value {
      artifact_spec {
        type {
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        }
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  outputs {
    key: "pre_transform_stats"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
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          properties {
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  outputs {
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    value {
      artifact_spec {
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  outputs {
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    value {
      artifact_spec {
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        }
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}
parameters {
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    value {
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  parameters {
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    value {
      field_value {
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}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
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}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-09-30T02:21:21.719173')
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp40g48lc_', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpo8x3j1uh', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403
WARNING:tensorflow:From /home/kbuilder/.local/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /home/kbuilder/.local/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature body_mass_g has a shape dim {
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}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
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}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
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}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmptk6d0x6b', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/ffa772c903d647cf8f103c11f62f4648/assets
2021-09-30 02:21:35.085334: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/ffa772c903d647cf8f103c11f62f4648/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/2239ae80d8274058ae85f3e958ea30e8/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/2239ae80d8274058ae85f3e958ea30e8/assets
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 4 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pre_transform_stats': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/pre_transform_stats/4"
custom_properties {
  key: "name"
  value {
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, artifact_type: name: "ExampleStatistics"
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)], 'transform_graph': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/transform_graph/4"
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custom_properties {
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, artifact_type: name: "TransformGraph"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:39.439133 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Transform is finished.
I0930 02:21:39.447779 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component ExampleValidator is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_validator.component.ExampleValidator"
  }
  id: "ExampleValidator"
}
contexts {
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  contexts {
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outputs {
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    value {
      artifact_spec {
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          properties {
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execution_options {
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INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:39.472927 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 5
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
  key: "tfx_version"
  value {
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state: LIVE
create_time_since_epoch: 1632968482943
last_update_time_since_epoch: 1632968482943
, artifact_type: id: 17
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)], 'statistics': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-transform/StatisticsGen/statistics/3"
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state: LIVE
create_time_since_epoch: 1632968484972
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, artifact_type: id: 19
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, artifact_type: name: "ExampleAnomalies"
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contexts {
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        artifact_query {
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outputs {
  outputs {
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    value {
      artifact_spec {
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          properties {
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execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-09-30T02:21:21.719173')
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to pipelines/penguin-transform/ExampleValidator/anomalies/5/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to pipelines/penguin-transform/ExampleValidator/anomalies/5/Split-eval.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 5 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-transform/ExampleValidator/anomalies/5"
custom_properties {
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, artifact_type: name: "ExampleAnomalies"
properties {
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properties {
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  value: STRING
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)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component ExampleValidator is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
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  id: "Trainer"
}
contexts {
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  contexts {
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    name {
      field_value {
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inputs {
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execution_options {
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INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:39.522161 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 6
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
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execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-09-30T02:21:21.719173')
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:udf_utils.get_fn {'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl', 'custom_config': 'null', 'eval_args': '{\n  "num_steps": 5\n}', 'train_args': '{\n  "num_steps": 100\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpchrflff3', 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+031ffa45b1192272539b1b5bcf48ac122829fc788a75b279ec3d7fb27bbdb403
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:culmen_length_mm_xf (InputLayer [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm_xf (InputLayer) [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm_xf (InputLaye [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:body_mass_g_xf (InputLayer)     [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 4)            0           culmen_length_mm_xf[0][0]        
INFO:absl:                                                                 culmen_depth_mm_xf[0][0]         
INFO:absl:                                                                 flipper_length_mm_xf[0][0]       
INFO:absl:                                                                 body_mass_g_xf[0][0]             
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 8)            40          concatenate[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 3)            27          dense_1[0][0]                    
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 1s 4ms/step - loss: 0.2656 - sparse_categorical_accuracy: 0.8910 - val_loss: 0.0306 - val_sparse_categorical_accuracy: 1.0000
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Trainer/model/6/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-transform/Trainer/model/6/Format-Serving. ModelRun written to pipelines/penguin-transform/Trainer/model_run/6
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 6 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model_run/6"
custom_properties {
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  value {
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, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model/6"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-09-30T02:21:21.719173:Trainer:model:0"
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custom_properties {
  key: "tfx_version"
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, artifact_type: name: "Model"
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:45.636586 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Trainer is finished.
I0930 02:21:45.640491 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is running.
INFO:absl:Running launcher for node_info {
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  id: "Pusher"
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contexts {
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I0930 02:21:45.662153 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 7
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=7, input_dict={'model': [Artifact(artifact: id: 13
type_id: 27
uri: "pipelines/penguin-transform/Trainer/model/6"
custom_properties {
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, artifact_type: name: "PushedModel"
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  type {
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contexts {
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outputs {
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    value {
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execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-09-30T02:21:21.719173')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1632968505
INFO:absl:Model written to serving path serving_model/penguin-transform/1632968505.
INFO:absl:Model pushed to pipelines/penguin-transform/Pusher/pushed_model/7.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 7 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-transform/Pusher/pushed_model/7"
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, artifact_type: name: "PushedModel"
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Pusher is finished.
I0930 02:21:45.691826 23080 rdbms_metadata_access_object.cc:686] No property is defined for the Type

「INFO:absl:ComponentPusherが終了しました」と表示されます。パイプラインが正常に終了した場合。

プッシャーコンポーネントはに訓練されたモデルをプッシュSERVING_MODEL_DIRあるserving_model/penguin-transform前の手順で変数を変更しなかった場合は、ディレクトリを。 Colabの左側のパネルにあるファイルブラウザから、または次のコマンドを使用して、結果を確認できます。

# List files in created model directory.
find {SERVING_MODEL_DIR}
serving_model/penguin-transform
serving_model/penguin-transform/1632968505
serving_model/penguin-transform/1632968505/assets
serving_model/penguin-transform/1632968505/saved_model.pb
serving_model/penguin-transform/1632968505/keras_metadata.pb
serving_model/penguin-transform/1632968505/variables
serving_model/penguin-transform/1632968505/variables/variables.index
serving_model/penguin-transform/1632968505/variables/variables.data-00000-of-00001

また、使用して生成されたモデルの署名を確認することができsaved_model_cliツールを

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

我々は定義されているのでserving_default私たち自身でserve_tf_examples_fn機能、それは単一の文字列を受け取り、署名を示しています。この文字列はtf.Examplesのシリアライズされた文字列であるとして解析されますtf.io.parse_example()我々は以前に定義されている(tf.Examplesの詳細情報機能ここ)。

エクスポートされたモデルをロードして、いくつかの例でいくつかの推論を試すことができます。

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

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

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4199581f90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f4245968ad0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4199581f90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f4245968ad0>).
# Prepare an example and run inference.
features = {
  'culmen_length_mm': tf.train.Feature(float_list=tf.train.FloatList(value=[49.9])),
  'culmen_depth_mm': tf.train.Feature(float_list=tf.train.FloatList(value=[16.1])),
  'flipper_length_mm': tf.train.Feature(int64_list=tf.train.Int64List(value=[213])),
  'body_mass_g': tf.train.Feature(int64_list=tf.train.Int64List(value=[5400])),
}
example_proto = tf.train.Example(features=tf.train.Features(feature=features))
examples = example_proto.SerializeToString()

result = inference_fn(examples=tf.constant([examples]))
print(result['output_0'].numpy())
[[-3.7887094 -0.8593055  5.310433 ]]

「Gentoo」種に対応する3番目の要素は、3つの中で最大になると予想されます。

次のステップ

あなたはより多くのまわり成分変換知りたい場合は、参照コンポーネントガイドを変換します。あなたは上でより多くのリソースを見つけることができますhttps://www.tensorflow.org/tfx/tutorials

参照してくださいTFXパイプラインが理解TFXの様々な概念についての詳細を学ぶために。