TFX 파이프라인을 사용하여 입력 데이터를 변환하고 모델을 학습시킵니다.
TensorFlow.org에서 보기
Google Colab에서 실행
GitHub에서 소스 보기
노트북 다운로드이 노트북 기반 자습서에서는 원시 입력 데이터를 수집하고 ML 교육에 적절하게 사전 처리하는 TFX 파이프라인을 생성 및 실행합니다. 이 노트북은 우리가 내장 된 TFX 파이프 라인을 기반으로 TFX 파이프 라인 및 TensorFlow 데이터 유효성 검사 자습서를 사용하여 데이터 유효성 검사 . 아직 읽지 않았다면 이 노트북을 계속 진행하기 전에 읽어야 합니다.
기능 엔지니어링을 통해 데이터의 예측 품질을 높이거나 차원을 줄일 수 있습니다. TFX 사용의 이점 중 하나는 변환 코드를 한 번만 작성하면 결과 변환이 훈련/서빙 스큐를 피하기 위해 훈련과 서빙 간에 일관성이 있다는 것입니다.
우리는이 추가됩니다 Transform 파이프 라인 구성 요소입니다. 변환 구성 요소는 사용하여 구현됩니다 tf.transform의 라이브러리를.
참조하시기 바랍니다 TFX 파이프 라인은 이해 TFX에서 다양한 개념에 대해 더 배울 수 있습니다.
설정
먼저 TFX Python 패키지를 설치하고 모델에 사용할 데이터 세트를 다운로드해야 합니다.
핍 업그레이드
로컬에서 실행할 때 시스템에서 Pip를 업그레이드하지 않으려면 Colab에서 실행 중인지 확인하세요. 물론 로컬 시스템은 별도로 업그레이드할 수 있습니다.
try:
import colab
!pip install --upgrade pip
except:
pass
TFX 설치
pip install -U tfx
런타임을 다시 시작하셨습니까?
Google Colab을 사용하는 경우 위의 셀을 처음 실행할 때 위의 "RESTART RUNTIME" 버튼을 클릭하거나 "런타임 > 런타임 다시 시작..." 메뉴를 사용하여 런타임을 다시 시작해야 합니다. Colab이 패키지를 로드하는 방식 때문입니다.
TensorFlow 및 TFX 버전을 확인하세요.
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
변수 설정
파이프라인을 정의하는 데 사용되는 몇 가지 변수가 있습니다. 이러한 변수를 원하는 대로 사용자 지정할 수 있습니다. 기본적으로 파이프라인의 모든 출력은 현재 디렉터리 아래에 생성됩니다.
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.
예시 데이터 준비
TFX 파이프라인에서 사용할 예제 데이터 세트를 다운로드합니다. 우리가 사용하고있는 데이터 세트는 팔머 펭귄 데이터 세트 .
그러나, 이미 전처리 된 데이터 집합을 사용 이전 튜토리얼과는 달리, 우리는 원시 팔머 펭귄 데이터 세트를 사용합니다.
TFX ExampleGen 구성 요소는 디렉터리에서 입력을 읽기 때문에 디렉터리를 만들고 데이터 집합을 디렉터리에 복사해야 합니다.
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>)
원시 데이터가 어떻게 보이는지 빠르게 살펴보십시오.
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
로 표현되는 값이 누락 된 일부 항목이 있습니다 NA . 이 자습서에서는 해당 항목만 삭제합니다.
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
펭귄을 설명하는 7가지 특징을 볼 수 있어야 합니다. 우리는 이전 튜토리얼과 동일한 기능 세트('culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g')를 사용하고 펭귄의 '종'을 예측할 것입니다.
유일한 차이점은 입력 데이터가 사전 처리되지 않는다는 것입니다. 이 튜토리얼에서는 'island' 또는 'sex'와 같은 다른 기능을 사용하지 않습니다.
스키마 파일 준비
에서 설명한 바와 같이 TFX 파이프 라인 및 TensorFlow 데이터 유효성 검사 자습서를 사용하여 데이터 검증 , 우리는 데이터 세트에 대한 스키마 파일이 필요합니다. 데이터세트가 이전 자습서와 다르기 때문에 다시 생성해야 합니다. 이 자습서에서는 이러한 단계를 건너뛰고 준비된 스키마 파일만 사용합니다.
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>)
이 스키마 파일은 수동 변경 없이 이전 자습서와 동일한 파이프라인으로 생성되었습니다.
파이프라인 생성
TFX 파이프라인은 Python API를 사용하여 정의됩니다. 우리는 추가 할 것이다 Transform 우리가에서 만든 파이프 라인 구성 요소 데이터 유효성 검사 튜토리얼 .
변환 컴포넌트는 데이터 입력에서 요구 ExampleGen 성분과의 스키마 SchemaGen 성분을, 그리고는 "그래프 변환"생성한다. 출력은 사용됩니다 Trainer 구성 요소입니다. 변환은 변환 후 구체화된 데이터인 "변환된 데이터"를 추가로 선택적으로 생성할 수 있습니다. 그러나 이 자습서에서는 중간 변환 데이터를 구체화하지 않고 훈련 중에 데이터를 변환합니다.
주의 할 점은 우리가 파이썬 함수를 정의 할 필요가 있다는 것입니다 preprocessing_fn 입력 데이터를 변환 할 방법을 설명 할 수있다. 이는 모델 정의를 위한 사용자 코드도 필요한 Trainer 구성 요소와 유사합니다.
전처리 및 학습 코드 작성
두 개의 Python 함수를 정의해야 합니다. 하나는 Transform용이고 하나는 Trainer용입니다.
전처리_fn
변환 구성 요소는 기능을 이름을 찾을 수 preprocessing_fn 우리가 그랬던 것처럼 주어진 모듈 파일에 Trainer 구성 요소입니다. 또한 사용하여 특정 기능을 지정할 수 있습니다 preprocessing_fn 매개 변수 변형 구성 요소를.
이 예에서는 두 가지 유형의 변환을 수행합니다. 같은 연속 숫자 기능에 대한 culmen_length_mm 및 body_mass_g , 우리는 사용하여 이러한 값을 정상화 tft.scale_to_z_score의 기능을. 레이블 기능의 경우 문자열 레이블을 숫자 인덱스 값으로 변환해야 합니다. 우리는 사용 tf.lookup.StaticHashTable 변환.
쉽게 변형 필드를 식별하기 위해, 우리는 추가 _xf 변환하는 기능 이름에 접미사.
run_fn
모델 자체는 이전 자습서와 거의 동일하지만 이번에는 Transform 구성 요소의 변환 그래프를 사용하여 입력 데이터를 변환합니다.
이전 튜토리얼과 비교하여 한 가지 더 중요한 차이점은 이제 모델의 계산 그래프뿐만 아니라 Transform 컴포넌트에서 생성되는 전처리를 위한 변환 그래프가 포함된 서빙용 모델을 내보낸다는 것입니다. 들어오는 요청을 처리하는 데 사용할 별도의 함수를 정의해야 합니다. 동일한 기능 것을 볼 수 있습니다 _apply_preprocessing 훈련 데이터와 서빙 요청에 모두 사용되었다.
_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
이제 TFX 파이프라인을 구축하기 위한 모든 준비 단계를 완료했습니다.
파이프라인 정의 작성
TFX 파이프라인을 생성하는 함수를 정의합니다. Pipeline 개체는 파이프 라인 오케스트레이션 시스템이 TFX 지원 중 하나를 사용하여 실행할 수있는 TFX 파이프 라인을 나타냅니다.
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)
파이프라인 실행
우리는 사용 LocalDagRunner 이전 튜토리얼한다.
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
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)]}), 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"
}
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name {
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contexts {
type {
name: "node"
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name {
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string_value: "penguin-transform.StatisticsGen"
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value {
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id: "CsvExampleGen"
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}
}
artifact_query {
type {
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}
}
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 {
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}
name {
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contexts {
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output_key: "result"
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outputs {
outputs {
key: "post_transform_anomalies"
value {
artifact_spec {
type {
name: "ExampleAnomalies"
properties {
key: "span"
value: INT
}
properties {
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}
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}
}
outputs {
key: "post_transform_schema"
value {
artifact_spec {
type {
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}
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}
outputs {
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value {
artifact_spec {
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name: "ExampleStatistics"
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key: "span"
value: INT
}
properties {
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outputs {
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value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
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}
properties {
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}
parameters {
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parameters {
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parameters {
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value {
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parameters {
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value {
field_value {
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}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
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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 {
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value {
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state: LIVE
create_time_since_epoch: 1638699713343
last_update_time_since_epoch: 1638699713343
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)], 'examples': [Artifact(artifact: id: 1
type_id: 15
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, 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 {
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contexts {
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name {
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}
}
contexts {
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name: "node"
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name {
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string_value: "penguin-transform.Transform"
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inputs {
inputs {
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context_queries {
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name {
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context_queries {
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name {
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artifact_query {
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context_queries {
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artifact_query {
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}
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 {
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}
}
}
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"
}
}
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}
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: "Schema"
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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"
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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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, 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"
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component ExampleValidator is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
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execution_options {
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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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upstream_nodes: "CsvExampleGen"
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execution_options {
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, 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"
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}
, artifact_type: name: "Model"
)], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-transform/Trainer/model_run/6"
custom_properties {
key: "name"
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"
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contexts {
contexts {
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name {
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contexts {
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contexts {
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context_queries {
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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"
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, artifact_type: name: "PushedModel"
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type {
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id: "Pusher"
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contexts {
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contexts {
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contexts {
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context_queries {
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value {
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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 {
string_value: "penguin-transform:2021-12-05T10:21:51.187624:Pusher:pushed_model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, 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
"INFO:absl:Component Pusher가 완료되었습니다."가 표시되어야 합니다. 파이프라인이 성공적으로 완료된 경우.
푸셔 구성 요소는에 훈련 모델을 밀어 SERVING_MODEL_DIR 는 IS serving_model/penguin-transform 이전 단계에서 변수를 변경하지 않은 경우 디렉토리를. Colab의 왼쪽 패널에서 또는 다음 명령을 사용하여 파일 브라우저에서 결과를 볼 수 있습니다.
# 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
또한 사용하여 생성 된 모델의 서명을 확인할 수 있습니다 saved_model_cli 도구를 .
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
우리는 정의하기 때문에 serving_default 우리 자신과 serve_tf_examples_fn 기능이 하나의 문자열을 사용 서명을 보여줍니다. 이 문자열은 tf.Examples의 직렬화 된 문자열과 함께 구문 분석됩니다 tf.io.parse_example () 우리가 이전에 정의 된 (더 tf.Examples에 대해 자세히 알아보기 기능 여기 ).
내보낸 모델을 로드하고 몇 가지 예를 통해 몇 가지 추론을 시도할 수 있습니다.
# 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]]
'젠투' 종에 해당하는 세 번째 요소는 세 가지 중 가장 클 것으로 예상됩니다.
다음 단계
더에 대한 구성 요소 변환 배우고 싶은 경우에, 참조 구성 요소 가이드를 변환 . 당신은 더 많은 자원을 찾을 수 있습니다 https://www.tensorflow.org/tfx/tutorials을
참조하시기 바랍니다 TFX 파이프 라인은 이해 TFX에서 다양한 개념에 대해 더 배울 수 있습니다.