টিএফএক্স পাইপলাইন এবং টেনসরফ্লো ট্রান্সফর্ম ব্যবহার করে ফিচার ইঞ্জিনিয়ারিং

ইনপুট ডেটা রূপান্তর করুন এবং একটি TFX পাইপলাইনের সাথে একটি মডেলকে প্রশিক্ষণ দিন।

TensorFlow.org এ দেখুন

Google Colab-এ চালান

GitHub-এ উৎস দেখুন

নোটবুক ডাউনলোড করুন

এই নোটবুক-ভিত্তিক টিউটোরিয়ালে, আমরা কাঁচা ইনপুট ডেটা গ্রহণ করার জন্য একটি TFX পাইপলাইন তৈরি করব এবং চালাব এবং এমএল প্রশিক্ষণের জন্য যথাযথভাবে এটিকে প্রিপ্রসেস করব। এই নোটবুক TFX পাইপলাইন আমরা সালে নির্মিত উপর ভিত্তি করে তৈরি ডেটা যাচাইকরণ TFX পাইপলাইন এবং TensorFlow ডেটা ভ্যালিডেশন টিউটোরিয়াল ব্যবহার । আপনি যদি এখনও এটি না পড়ে থাকেন তবে এই নোটবুকটি নিয়ে এগিয়ে যাওয়ার আগে আপনার এটি পড়া উচিত।

আপনি আপনার ডেটার ভবিষ্যদ্বাণীমূলক গুণমান বাড়াতে এবং/অথবা বৈশিষ্ট্য প্রকৌশলের মাধ্যমে মাত্রা কমাতে পারেন। TFX ব্যবহার করার একটি সুবিধা হল যে আপনি একবার আপনার ট্রান্সফর্মেশন কোড লিখবেন, এবং ফলস্বরূপ ট্রান্সফর্মগুলি প্রশিক্ষণ এবং পরিবেশনের মধ্যে সামঞ্জস্যপূর্ণ হবে যাতে প্রশিক্ষণ/সার্ভিং স্কু এড়ানো যায়।

আমরা একটি যোগ হবে Transform পাইপলাইন অংশ। ট্রান্সফর্ম উপাদান ব্যবহার বাস্তবায়িত হয় tf.transform গ্রন্থাগার।

দয়া করে দেখুন TFX পাইপলাইন বুঝুন TFX বিভিন্ন ধারণা সম্পর্কে আরো জানতে।

সেট আপ করুন

আমাদের প্রথমে TFX পাইথন প্যাকেজ ইনস্টল করতে হবে এবং ডেটাসেটটি ডাউনলোড করতে হবে যা আমরা আমাদের মডেলের জন্য ব্যবহার করব।

পিপ আপগ্রেড করুন

স্থানীয়ভাবে চালানোর সময় একটি সিস্টেমে পিপ আপগ্রেড করা এড়াতে, আমরা Colab-এ চালাচ্ছি কিনা তা নিশ্চিত করুন। স্থানীয় সিস্টেম অবশ্যই আলাদাভাবে আপগ্রেড করা যেতে পারে।

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

TFX ইনস্টল করুন

pip install -U tfx

আপনি কি রানটাইম রিস্টার্ট করেছেন?

আপনি যদি Google Colab ব্যবহার করেন, প্রথমবার যখন আপনি উপরের সেলটি চালান, তাহলে আপনাকে অবশ্যই উপরে "রিস্টার্ট RUNTIME" বোতামে ক্লিক করে বা "রানটাইম > রানটাইম রিস্টার্ট..." মেনু ব্যবহার করে রানটাইম রিস্টার্ট করতে হবে। Colab যেভাবে প্যাকেজগুলি লোড করে তার কারণেই এটি হয়েছে৷

TensorFlow এবং TFX সংস্করণ পরীক্ষা করুন।

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

TensorFlow version: 2.6.2
TFX version: 1.4.0

ভেরিয়েবল সেট আপ করুন

একটি পাইপলাইন সংজ্ঞায়িত করতে ব্যবহৃত কিছু ভেরিয়েবল আছে। আপনি আপনার ইচ্ছা মত এই ভেরিয়েবল কাস্টমাইজ করতে পারেন. ডিফল্টরূপে পাইপলাইন থেকে সমস্ত আউটপুট বর্তমান ডিরেক্টরির অধীনে তৈরি করা হবে।

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.

উদাহরণ ডেটা প্রস্তুত করুন

আমরা আমাদের TFX পাইপলাইনে ব্যবহারের জন্য উদাহরণ ডেটাসেট ডাউনলোড করব। ডেটা সেটটি আমরা ব্যবহার করা হয় পামার পেঙ্গুইনদের ডেটা সেটটি ।

যাইহোক, পূর্ববর্তী টিউটোরিয়াল যা ইতিমধ্যে preprocessed ডেটা সেটটি ব্যবহার করা অসদৃশ, আমরা কাঁচা পামার পেঙ্গুইনদের ডেটা সেটটি ব্যবহার করা হবে।

যেহেতু TFX ExampleGen কম্পোনেন্ট একটি ডিরেক্টরি থেকে ইনপুট পড়ে, আমাদের একটি ডিরেক্টরি তৈরি করতে হবে এবং এতে ডেটাসেটটি কপি করতে হবে।

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-dataacmxfq9f/data.csv', <http.client.HTTPMessage at 0x7f5b0ab1bf10>)

কাঁচা ডেটা কেমন দেখাচ্ছে তা দ্রুত দেখে নিন।

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

সেখানে মান যা হিসাবে প্রতিনিধিত্ব করা হয় অনুপস্থিত সঙ্গে কিছু এন্ট্রি NA । আমরা এই টিউটোরিয়ালে সেই এন্ট্রিগুলি মুছে দেব।

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

আপনি সাতটি বৈশিষ্ট্য দেখতে সক্ষম হবেন যা পেঙ্গুইনকে বর্ণনা করে। আমরা আগের টিউটোরিয়ালগুলির মতো বৈশিষ্ট্যগুলির একই সেট ব্যবহার করব - 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g' - এবং একটি পেঙ্গুইনের 'প্রজাতি' সম্পর্কে ভবিষ্যদ্বাণী করব।

শুধুমাত্র পার্থক্য হবে যে ইনপুট ডেটা প্রিপ্রসেস করা হয় না। মনে রাখবেন যে আমরা এই টিউটোরিয়ালে 'দ্বীপ' বা 'সেক্স' এর মতো অন্যান্য বৈশিষ্ট্যগুলি ব্যবহার করব না।

একটি স্কিমা ফাইল প্রস্তুত করুন

বর্ণনা অনুযায়ী ডেটা যাচাইকরণ TFX পাইপলাইন এবং TensorFlow ডেটা ভ্যালিডেশন টিউটোরিয়াল ব্যবহার , আমরা ডেটা সেটটি জন্য স্কিমা ফাইল প্রয়োজন। কারণ ডেটাসেটটি আগের টিউটোরিয়াল থেকে ভিন্ন আমাদের এটি আবার তৈরি করতে হবে। এই টিউটোরিয়ালে, আমরা সেই ধাপগুলি এড়িয়ে যাব এবং শুধুমাত্র একটি প্রস্তুত স্কিমা ফাইল ব্যবহার করব।

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 0x7f5b0ab20f50>)

এই স্কিমা ফাইলটি আগের টিউটোরিয়ালের মতো একই পাইপলাইন দিয়ে তৈরি করা হয়েছে কোনো ম্যানুয়াল পরিবর্তন ছাড়াই।

একটি পাইপলাইন তৈরি করুন

TFX পাইপলাইনগুলি Python API ব্যবহার করে সংজ্ঞায়িত করা হয়। আমরা যোগ হবে Transform পাইপলাইন আমরা তৈরি করতে কম্পোনেন্ট ডেটা ভ্যালিডেশন টিউটোরিয়াল ।

একজন ট্রান্সফর্ম উপাদান একটি থেকে ইনপুট ডেটার প্রয়োজন ExampleGen উপাদান এবং একটি থেকে একটি স্কিমা SchemaGen উপাদান, এবং একটি "গ্রাফ রুপান্তর" উৎপন্ন হয়। আউটপুট একটি ব্যবহার করা হবে Trainer অংশ। ট্রান্সফর্ম ঐচ্ছিকভাবে "রূপান্তরিত ডেটা" তৈরি করতে পারে, যা রূপান্তরের পরে বস্তুগত ডেটা। যাইহোক, আমরা এই টিউটোরিয়ালে প্রশিক্ষণের সময় ডেটা রূপান্তর করব মধ্যবর্তী রূপান্তরিত ডেটার বাস্তবায়ন ছাড়াই।

নোটটিতে একটি বিষয় যে আমরা একটি পাইথন ফাংশন, সংজ্ঞায়িত করতে প্রয়োজন preprocessing_fn বর্ণনা করতে কিভাবে ইনপুট ডেটা রুপান্তরিত করতে হবে। এটি একটি প্রশিক্ষক উপাদানের অনুরূপ যার মডেল সংজ্ঞার জন্য ব্যবহারকারীর কোডও প্রয়োজন।

প্রিপ্রসেসিং এবং ট্রেনিং কোড লিখুন

আমাদের দুটি পাইথন ফাংশন সংজ্ঞায়িত করতে হবে। একটি ট্রান্সফর্মের জন্য এবং একটি প্রশিক্ষকের জন্য।

প্রিপ্রসেসিং_এফএন

ট্রান্সফর্ম উপাদান নামে একটি ফাংশন পাবেন preprocessing_fn হিসাবে আমরা জন্য করেছিলাম দেওয়া মডিউল ফাইলের মধ্যে Trainer অংশ। এছাড়াও আপনি ব্যবহার করে একটি নির্দিষ্ট ফাংশন নির্দিষ্ট করতে পারেন preprocessing_fn প্যারামিটার ট্রান্সফর্ম উপাদানের।

এই উদাহরণে, আমরা দুই ধরনের রূপান্তর করব। মত একটানা সাংখ্যিক বৈশিষ্ট্যের জন্য culmen_length_mm এবং body_mass_g , আমরা এই ব্যবহার মান স্বাভাবিক হবে tft.scale_to_z_score ফাংশন। লেবেল বৈশিষ্ট্যের জন্য, আমাদের স্ট্রিং লেবেলগুলিকে সংখ্যাসূচক সূচক মানগুলিতে রূপান্তর করতে হবে। আমরা ব্যবহার করবে tf.lookup.StaticHashTable রূপান্তরের জন্য।

সহজে রুপান্তরিত ক্ষেত্র চিহ্নিত করার, আমরা একটি যোগ _xf রুপান্তরিত বৈশিষ্ট্য নামের প্রত্যয়।

রান_এফএন

মডেলটি আগের টিউটোরিয়ালগুলির মতোই প্রায় একই, তবে এবার আমরা ট্রান্সফর্ম কম্পোনেন্ট থেকে ট্রান্সফর্ম গ্রাফ ব্যবহার করে ইনপুট ডেটা রূপান্তর করব।

আগের টিউটোরিয়ালের তুলনায় আরও একটি গুরুত্বপূর্ণ পার্থক্য হল যে এখন আমরা পরিবেশনের জন্য একটি মডেল রপ্তানি করি যাতে শুধুমাত্র মডেলের কম্পিউটেশন গ্রাফ নয়, প্রিপ্রসেসিংয়ের জন্য ট্রান্সফর্ম গ্রাফও অন্তর্ভুক্ত থাকে, যা ট্রান্সফর্ম কম্পোনেন্টে তৈরি হয়। আমাদের একটি পৃথক ফাংশন সংজ্ঞায়িত করতে হবে যা আগত অনুরোধগুলি পরিবেশন করার জন্য ব্যবহার করা হবে। আপনি দেখতে পারেন যে একই ফাংশন _apply_preprocessing প্রশিক্ষণ ডেটা এবং ভজনা অনুরোধ উভয়ের জন্য ব্যবহার করা হয়েছিল।

_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: 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[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[_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(_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=key)
      for key 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

এখন আপনি একটি TFX পাইপলাইন তৈরির সমস্ত প্রস্তুতিমূলক পদক্ষেপগুলি সম্পন্ন করেছেন৷

পাইপলাইনের সংজ্ঞা লিখ

আমরা একটি TFX পাইপলাইন তৈরি করার জন্য একটি ফাংশন সংজ্ঞায়িত করি। একটি Pipeline বস্তুর একটি TFX পাইপলাইন, যা পাইপলাইন অর্কেস্ট্রারচনা সিস্টেমগুলি TFX সমর্থন একটি ব্যবহার চলবে প্রতিনিধিত্ব করে।

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)

পাইপলাইন চালান

আমরা ব্যবহার করবে LocalDagRunner পূর্ববর্তী টিউটোরিয়ালে হিসাবে।

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 a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp_rl2wpg3/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmps7emqvj6', '--dist-dir', '/tmp/tmpnvanprdd']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
listing git files failed - pretending there aren't any
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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 a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpi9sy085o/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpugc_ecw_', '--dist-dir', '/tmp/tmpr1xz5bg6']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_utils.py -> build/lib
installing to /tmp/tmps7emqvj6
running install
running install_lib
copying build/lib/penguin_utils.py -> /tmp/tmps7emqvj6
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/tmps7emqvj6/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3.7.egg-info
running install_scripts
creating /tmp/tmps7emqvj6/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL
creating '/tmp/tmpnvanprdd/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' and adding '/tmp/tmps7emqvj6' to it
adding 'penguin_utils.py'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/RECORD'
removing /tmp/tmps7emqvj6
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
listing git files failed - pretending there aren't any
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'
INFO:absl:Using deployment config:
 executor_specs {
  key: "CsvExampleGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "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: "2021-12-05T10:21:51.187624"
      }
    }
  }
  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-dataacmxfq9f"
      }
    }
  }
  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
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_utils.py -> build/lib
installing to /tmp/tmpugc_ecw_
running install
running install_lib
copying build/lib/penguin_utils.py -> /tmp/tmpugc_ecw_
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/tmpugc_ecw_/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3.7.egg-info
running install_scripts
creating /tmp/tmpugc_ecw_/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL
creating '/tmp/tmpr1xz5bg6/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' and adding '/tmp/tmpugc_ecw_' to it
adding 'penguin_utils.py'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9.dist-info/RECORD'
removing /tmp/tmpugc_ecw_
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 10:21:51.351922 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:21:52.158721 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:21:52.173334 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:21:52.180279 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
I1205 10:21:52.194584 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1638699709,sum_checksum:1638699709"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624: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={'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}', 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_file_format': 5, 'output_data_format': 6, 'input_base': '/tmp/tfx-dataacmxfq9f', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:13161,xor_checksum:1638699709,sum_checksum:1638699709'}, 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/2021-12-05T10:21:51.187624', 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: "2021-12-05T10:21:51.187624"
      }
    }
  }
  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-dataacmxfq9f"
      }
    }
  }
  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='2021-12-05T10:21:51.187624')
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-dataacmxfq9f/* 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:1638699709,sum_checksum:1638699709"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, 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: "2021-12-05T10:21:51.187624"
      }
    }
  }
  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
I1205 10:21:53.330585 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Processing source uri: schema, properties: {}, custom_properties: {}
I1205 10:21:53.340232 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component schema_importer is finished.
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: "2021-12-05T10:21:51.187624"
      }
    }
  }
  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: "2021-12-05T10:21:51.187624"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
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
I1205 10:21:53.360662 24712 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:1638699709,sum_checksum:1638699709"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624: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: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638699713316
last_update_time_since_epoch: 1638699713316
, 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:2021-12-05T10:21:51.187624: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/2021-12-05T10:21:51.187624', 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: "2021-12-05T10:21:51.187624"
      }
    }
  }
  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: "2021-12-05T10:21:51.187624"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
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 {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
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"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}) 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"
  }
  id: "Transform"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:21:51.187624"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.Transform"
      }
    }
  }
}
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: "2021-12-05T10:21:51.187624"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
  inputs {
    key: "schema"
    value {
      channels {
        producer_node_query {
          id: "schema_importer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-transform"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
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          name {
            field_value {
              string_value: "2021-12-05T10:21:51.187624"
            }
          }
        }
        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"
      }
      min_count: 1
    }
  }
}
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 {
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      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
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}

INFO:absl:MetadataStore with DB connection initialized
I1205 10:21:56.029392 24712 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={'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638699713343
last_update_time_since_epoch: 1638699713343
, artifact_type: id: 17
name: "Schema"
)], 'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
properties {
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  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
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  value {
    string_value: "tfrecords_gzip"
  }
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custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:13161,xor_checksum:1638699709,sum_checksum:1638699709"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:CsvExampleGen:examples:0"
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custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
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custom_properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1638699713316
last_update_time_since_epoch: 1638699713316
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
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}
properties {
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}
)]}, output_dict=defaultdict(<class 'list'>, {'updated_analyzer_cache': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/updated_analyzer_cache/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Transform:updated_analyzer_cache:0"
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, artifact_type: name: "TransformCache"
)], 'post_transform_stats': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/post_transform_stats/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Transform:post_transform_stats:0"
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, artifact_type: name: "ExampleStatistics"
properties {
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properties {
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)], 'pre_transform_stats': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/pre_transform_stats/4"
custom_properties {
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}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)], 'pre_transform_schema': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/pre_transform_schema/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Transform:pre_transform_schema:0"
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}
, artifact_type: name: "Schema"
)], 'post_transform_anomalies': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/post_transform_anomalies/4"
custom_properties {
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  }
}
, artifact_type: name: "ExampleAnomalies"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)], 'transform_graph': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/transform_graph/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Transform:transform_graph:0"
  }
}
, artifact_type: name: "TransformGraph"
)], 'post_transform_schema': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/post_transform_schema/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Transform:post_transform_schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'disable_statistics': 0, 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'custom_config': 'null', 'force_tf_compat_v1': 0}, execution_output_uri='pipelines/penguin-transform/Transform/.system/executor_execution/4/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/Transform/.system/stateful_working_dir/2021-12-05T10:21:51.187624', tmp_dir='pipelines/penguin-transform/Transform/.system/executor_execution/4/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.transform.component.Transform"
  }
  id: "Transform"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:21:51.187624"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        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"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T10:21:51.187624"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
  inputs {
    key: "schema"
    value {
      channels {
        producer_node_query {
          id: "schema_importer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
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              string_value: "penguin-transform"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T10:21:51.187624"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.schema_importer"
            }
          }
        }
        artifact_query {
          type {
            name: "Schema"
          }
        }
        output_key: "result"
      }
      min_count: 1
    }
  }
}
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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp3elppure', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpctb52fyz', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl'.
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpgv9zk7st', 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Transform-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature 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: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:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
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], int] instead.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: key_value_init/LookupTableImportV2
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.
2021-12-05 10:22:06.547139: 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/167780659a644435abe6c969ed4771de/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/167780659a644435abe6c969ed4771de/assets
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:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/cbe53dc813ec4d51a99f25099bd3736e/assets
INFO:tensorflow:Assets written to: pipelines/penguin-transform/Transform/transform_graph/4/.temp_path/tftransform_tmp/cbe53dc813ec4d51a99f25099bd3736e/assets
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:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
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'>, {'updated_analyzer_cache': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/updated_analyzer_cache/4"
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, artifact_type: name: "TransformGraph"
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custom_properties {
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, artifact_type: name: "Schema"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:11.698540 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:22:11.707963 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Transform is finished.
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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      field_value {
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    value {
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upstream_nodes: "schema_importer"
execution_options {
  caching_options {
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INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:11.732254 24712 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 {
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state: LIVE
create_time_since_epoch: 1638699713343
last_update_time_since_epoch: 1638699713343
, artifact_type: id: 17
name: "Schema"
)], 'statistics': [Artifact(artifact: id: 3
type_id: 19
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state: LIVE
create_time_since_epoch: 1638699716011
last_update_time_since_epoch: 1638699716011
, artifact_type: id: 19
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, artifact_type: name: "ExampleAnomalies"
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outputs {
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    value {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
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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properties {
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properties {
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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 {
  type {
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  id: "Trainer"
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contexts {
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inputs {
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            name: "pipeline"
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INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:11.785852 24712 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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create_time_since_epoch: 1638699713316
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, artifact_type: id: 15
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)], 'transform_graph': [Artifact(artifact: id: 9
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, artifact_type: name: "ModelRun"
)]}), exec_properties={'custom_config': 'null', 'train_args': '{\n  "num_steps": 100\n}', 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'eval_args': '{\n  "num_steps": 5\n}'}, execution_output_uri='pipelines/penguin-transform/Trainer/.system/executor_execution/6/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/Trainer/.system/stateful_working_dir/2021-12-05T10:21:51.187624', tmp_dir='pipelines/penguin-transform/Trainer/.system/executor_execution/6/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.trainer.component.Trainer"
  }
  id: "Trainer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-transform"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
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    name {
      field_value {
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    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-transform.Trainer"
      }
    }
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inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
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          name {
            field_value {
              string_value: "penguin-transform"
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          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T10:21:51.187624"
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        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-transform.CsvExampleGen"
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          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
  inputs {
    key: "transform_graph"
    value {
      channels {
        producer_node_query {
          id: "Transform"
        }
        context_queries {
          type {
            name: "pipeline"
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          name {
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        context_queries {
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        context_queries {
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            field_value {
              string_value: "penguin-transform.Transform"
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        }
        artifact_query {
          type {
            name: "TransformGraph"
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        output_key: "transform_graph"
      }
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  }
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outputs {
  outputs {
    key: "model"
    value {
      artifact_spec {
        type {
          name: "Model"
        }
      }
    }
  }
  outputs {
    key: "model_run"
    value {
      artifact_spec {
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parameters {
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  parameters {
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  parameters {
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      field_value {
        string_value: "penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl"
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  parameters {
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      field_value {
        string_value: "{\n  \"num_steps\": 100\n}"
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upstream_nodes: "CsvExampleGen"
upstream_nodes: "Transform"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:udf_utils.get_fn {'custom_config': 'null', 'train_args': '{\n  "num_steps": 100\n}', 'module_path': 'penguin_utils@pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl', 'eval_args': '{\n  "num_steps": 5\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpfnmreae0', 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl']
Processing ./pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-transform/_wheels/tfx_user_code_Trainer-0.0+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9-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+a5e9139bd7facf5026b5306a6aea534f89db0dea58ebe1bb1fb5ebb9df5fdea9
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
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 (InputLayer)   [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer)    [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer)  [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:body_mass_g (InputLayer)        [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 4)            0           culmen_length_mm[0][0]           
INFO:absl:                                                                 culmen_depth_mm[0][0]            
INFO:absl:                                                                 flipper_length_mm[0][0]          
INFO:absl:                                                                 body_mass_g[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 8)            40          concatenate[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 3)            27          dense_1[0][0]                    
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 1s 4ms/step - loss: 0.2132 - sparse_categorical_accuracy: 0.9490 - val_loss: 0.0102 - 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': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model/6"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Trainer:model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "Model"
)], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model_run/6"
custom_properties {
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  value {
    string_value: "penguin-transform:2021-12-05T10:21:51.187624:Trainer:model_run:0"
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custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
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, artifact_type: name: "ModelRun"
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:18.036643 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Trainer is finished.
I1205 10:22:18.041664 24712 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 {
  type {
    name: "tfx.components.pusher.component.Pusher"
  }
  id: "Pusher"
}
contexts {
  contexts {
    type {
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    name {
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  contexts {
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  contexts {
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    name {
      field_value {
        string_value: "penguin-transform.Pusher"
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inputs {
  inputs {
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      channels {
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          id: "Trainer"
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        context_queries {
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        artifact_query {
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}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
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parameters {
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  parameters {
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        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-transform\"\n  }\n}"
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upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
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INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:18.063011 24712 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 7
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=7, input_dict={'model': [Artifact(artifact: id: 12
type_id: 26
uri: "pipelines/penguin-transform/Trainer/model/6"
custom_properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1638699738045
last_update_time_since_epoch: 1638699738045
, artifact_type: id: 26
name: "Model"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-transform/Pusher/pushed_model/7"
custom_properties {
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  }
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, artifact_type: name: "PushedModel"
)]}), exec_properties={'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-transform"\n  }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-transform/Pusher/.system/executor_execution/7/executor_output.pb', stateful_working_dir='pipelines/penguin-transform/Pusher/.system/stateful_working_dir/2021-12-05T10:21:51.187624', tmp_dir='pipelines/penguin-transform/Pusher/.system/executor_execution/7/.temp/', pipeline_node=node_info {
  type {
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  id: "Pusher"
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contexts {
  contexts {
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  contexts {
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inputs {
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    value {
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          id: "Trainer"
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        context_queries {
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        context_queries {
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        context_queries {
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        artifact_query {
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            name: "Model"
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        output_key: "model"
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outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
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          name: "PushedModel"
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parameters {
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upstream_nodes: "Trainer"
execution_options {
  caching_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1638699738
INFO:absl:Model written to serving path serving_model/penguin-transform/1638699738.
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"
custom_properties {
  key: "name"
  value {
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custom_properties {
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}
, artifact_type: name: "PushedModel"
)]}) for execution 7
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Pusher is finished.
I1205 10:22:18.092860 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type

আপনার দেখতে হবে "INFO:absl:কম্পোনেন্ট পুশার শেষ হয়েছে।" যদি পাইপলাইন সফলভাবে শেষ হয়।

বিমানপোত উপাদান প্রশিক্ষিত মডেল push কর্মের SERVING_MODEL_DIR যা serving_model/penguin-transform ডিরেক্টরির যদি আপনি আগের পদক্ষেপে ভেরিয়েবল পরিবর্তন হয়নি। আপনি Colab-এর বাম দিকের প্যানেলে বা নিম্নলিখিত কমান্ড ব্যবহার করে ফাইল ব্রাউজার থেকে ফলাফল দেখতে পারেন:

# List files in created model directory.
find {SERVING_MODEL_DIR}

serving_model/penguin-transform
serving_model/penguin-transform/1638699738
serving_model/penguin-transform/1638699738/keras_metadata.pb
serving_model/penguin-transform/1638699738/assets
serving_model/penguin-transform/1638699738/variables
serving_model/penguin-transform/1638699738/variables/variables.data-00000-of-00001
serving_model/penguin-transform/1638699738/variables/variables.index
serving_model/penguin-transform/1638699738/saved_model.pb

এছাড়াও আপনি ব্যবহার উত্পন্ন মডেলের স্বাক্ষর পরীক্ষা করতে পারবেন saved_model_cli টুল ।

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

যেহেতু আমরা সংজ্ঞায়িত serving_default আমাদের নিজস্ব সঙ্গে serve_tf_examples_fn ফাংশন, স্বাক্ষর শো যে এটি একটি একক পংক্তি লাগে। এই স্ট্রিং tf.Examples একটি ধারাবাহিকভাবে স্ট্রিং এবং বিশ্লেষণ করা হবে tf.io.parse_example () ফাংশন হিসাবে আমরা আগেই সংজ্ঞায়িত (tf.Examples সম্পর্কে আরো জানতে এখানে )।

আমরা রপ্তানি করা মডেল লোড করতে পারি এবং কয়েকটি উদাহরণ সহ কিছু অনুমান চেষ্টা করতে পারি।

# 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 (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f5b0836e3d0> and <keras.engine.input_layer.InputLayer object at 0x7f5b091aa550>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f5b0836e3d0> and <keras.engine.input_layer.InputLayer object at 0x7f5b091aa550>).

# 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())

[[-2.5357873 -3.0600576  3.4993587]]

তৃতীয় উপাদান, যা 'জেন্টু' প্রজাতির সাথে মিলে যায়, তিনটির মধ্যে সবচেয়ে বড় বলে আশা করা হচ্ছে।

পরবর্তী পদক্ষেপ

আপনি আরো জানুন উপাদান ট্রান্সফর্ম জানতে চান, দেখুন কম্পোনেন্ট নির্দেশিকা ট্রান্সফর্ম । আপনি আরও রিসোর্স জানতে পারেন https://www.tensorflow.org/tfx/tutorials

দয়া করে দেখুন TFX পাইপলাইন বুঝুন TFX বিভিন্ন ধারণা সম্পর্কে আরো জানতে।