Giriş verilerini dönüştürün ve bir modeli TFX ardışık düzeniyle eğitin.
 TensorFlow.org'da görüntüleyin |  Google Colab'da çalıştırın |  Kaynağı GitHub'da görüntüleyin |  Not defterini indir |
Bu not defteri tabanlı öğreticide, ham girdi verilerini almak ve bunları makine öğrenimi eğitimi için uygun şekilde önceden işlemek için bir TFX ardışık düzeni oluşturup çalıştıracağız. Bu defter biz inşa TFX boru hattı dayanmaktadır TFX Boru Hattı ve TensorFlow Veri Doğrulama Eğitimi kullanarak veri doğrulama . Henüz okumadıysanız, bu deftere geçmeden önce okumalısınız.
Özellik mühendisliği ile verilerinizin tahmine dayalı kalitesini artırabilir ve/veya boyutluluğu azaltabilirsiniz. TFX kullanmanın faydalarından biri, dönüşüm kodunuzu bir kez yazmanız ve eğitim/hizmet çarpıklığını önlemek için elde edilen dönüşümlerin eğitim ve sunum arasında tutarlı olmasıdır.
Biz katacak Transform boru hattına bileşeni. Transform bileşeni kullanılarak uygulanır tf.transform kütüphanesi.
Bakınız TFX Boru hatları anlama Tfx çeşitli kavramlar hakkında daha fazla bilgi edinmek.
Kurmak
Öncelikle TFX Python paketini kurmamız ve modelimiz için kullanacağımız veri setini indirmemiz gerekiyor.
Pip'i Yükselt
Yerel olarak çalışırken bir sistemde Pip'i yükseltmekten kaçınmak için Colab'da çalıştığımızdan emin olun. Yerel sistemler elbette ayrı ayrı yükseltilebilir.
try:
import colab
!pip install --upgrade pip
except:
pass
TFX'i yükleyin
pip install -U tfx
Çalışma zamanını yeniden başlattınız mı?
Google Colab kullanıyorsanız, yukarıdaki hücreyi ilk kez çalıştırdığınızda, yukarıdaki "ÇALIŞTIRMA ZAMINI YENİDEN BAŞLAT" düğmesini tıklayarak veya "Çalışma Zamanı > Çalışma zamanını yeniden başlat ..." menüsünü kullanarak çalışma zamanını yeniden başlatmanız gerekir. Bunun nedeni Colab'ın paketleri yükleme şeklidir.
TensorFlow ve TFX sürümlerini kontrol edin.
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
Değişkenleri ayarla
Bir boru hattını tanımlamak için kullanılan bazı değişkenler vardır. Bu değişkenleri istediğiniz gibi özelleştirebilirsiniz. Varsayılan olarak, işlem hattından gelen tüm çıktılar geçerli dizin altında oluşturulacaktır.
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.
Örnek verileri hazırlayın
TFX ardışık düzenimizde kullanmak için örnek veri kümesini indireceğiz. Kullandığımız veri kümesi olan Palmer Penguenler veri kümesi .
Ancak, zaten ön işlenen veri kümesi kullanılan önceki öğreticiler aksine çiğ Palmer Penguenler veri kümesi kullanır.
TFX ExampleGen bileşeni bir dizinden girdileri okuduğundan, bir dizin oluşturmamız ve veri kümesini ona kopyalamamız gerekir.
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>)
Ham verilerin nasıl göründüğüne hızlıca bir göz atın.
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
Olarak temsil edilir değerleri eksik olan bazı kayıtlar vardır NA . Bu eğitimdeki bu girişleri sileceğiz.
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
Penguenleri tanımlayan yedi özelliği görebilmeniz gerekir. Önceki derslerle aynı özellikleri kullanacağız - 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g' - ve bir penguenin 'türlerini' tahmin edeceğiz.
Tek fark, giriş verilerinin önceden işlenmemiş olmasıdır. Bu eğitimde 'ada' veya 'seks' gibi diğer özellikleri kullanmayacağımızı unutmayın.
Bir şema dosyası hazırlayın
Anlatıldığı gibi TFX Boru Hattı ve TensorFlow Veri Doğrulama Eğitimi kullanarak Veri doğrulama , biz veri kümesi için bir şema dosyası gerekir. Veri seti önceki öğreticiden farklı olduğu için onu tekrar oluşturmamız gerekiyor. Bu derste, bu adımları atlayacağız ve sadece hazırlanmış bir şema dosyası kullanacağız.
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>)
Bu şema dosyası, herhangi bir manuel değişiklik yapılmadan önceki öğreticidekiyle aynı işlem hattıyla oluşturulmuştur.
Bir işlem hattı oluşturun
TFX ardışık düzenleri Python API'leri kullanılarak tanımlanır. Biz eklemeyi yapar Transform biz oluşturulan boru hattına bileşeni Veri Doğrulama öğretici .
Bir dönüştürme bileşeni bir giriş verilerini gerektirir ExampleGen bileşeni ve bir bir şema SchemaGen bileşeni ve bir "grafiği dönüşümü" üretir. Çıktısı kullanılacak Trainer bileşeni. Dönüştürme, isteğe bağlı olarak, dönüşümden sonra somutlaşan veriler olan "dönüştürülmüş veriler" de üretebilir. Ancak, bu öğreticide eğitim sırasında verileri, ara dönüştürülmüş verileri somutlaştırmadan dönüştüreceğiz.
Nota bir şey biz bir Python işlevini tanımlamak gerekir olmasıdır preprocessing_fn giriş veri dönüştürülmesi gerektiğini nasıl açıklamak için. Bu, aynı zamanda model tanımı için kullanıcı kodu gerektiren bir Eğitmen bileşenine benzer.
Ön işleme ve eğitim kodu yazın
İki Python işlevi tanımlamamız gerekiyor. Biri Dönüştürme için, diğeri Eğitmen için.
önişleme_fn
Transform bileşeni fonksiyonunu adında bulacaksınız preprocessing_fn biz yaptığımız gibi verilen modül dosyasında Trainer bileşeni. Ayrıca kullanarak belirli bir işlev belirtebilirsiniz preprocessing_fn parametreyi Transform bileşenin.
Bu örnekte iki tür dönüşüm yapacağız. Gibi sürekli sayısal özellikler için culmen_length_mm ve body_mass_g , biz kullanarak bu değerleri normale olacaktır tft.scale_to_z_score işlevi. Etiket özelliği için string etiketlerini sayısal indeks değerlerine dönüştürmemiz gerekiyor. Biz kullanacağız tf.lookup.StaticHashTable dönüşüm için.
Kolayca dönüştürülmüş alanları belirlemek için, bir ekleme _xf dönüştürülmüş özellik adlarına son ek.
run_fn
Modelin kendisi önceki öğreticilerdekiyle hemen hemen aynıdır, ancak bu sefer Transform bileşenindeki dönüşüm grafiğini kullanarak girdi verilerini dönüştüreceğiz.
Önceki öğreticiye kıyasla daha önemli bir fark, artık yalnızca modelin hesaplama grafiğini değil, aynı zamanda Dönüştür bileşeninde oluşturulan ön işleme için dönüşüm grafiğini de içeren bir modeli hizmet için dışa aktarıyor olmamızdır. Gelen isteklere hizmet vermek için kullanılacak ayrı bir fonksiyon tanımlamamız gerekiyor. Aynı işlevi olduğunu görebilirsiniz _apply_preprocessing eğitim verileri ve hizmet talebinin ikisi için kullanıldı.
_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
Artık bir TFX işlem hattı oluşturmak için tüm hazırlık adımlarını tamamladınız.
Bir işlem hattı tanımı yazın
Bir TFX boru hattı oluşturmak için bir fonksiyon tanımlıyoruz. Bir Pipeline nesne hattı düzenleme sistemleri bu TFX desteklerinden biri kullanılarak çalıştırılabilir bir TFX boru hattı temsil etmektedir.
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)
Boru hattını çalıştırın
Biz kullanacağız LocalDagRunner önceki öğretici olduğu gibi.
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"
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contexts {
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name {
field_value {
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}
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contexts {
type {
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name {
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string_value: "penguin-transform.Transform"
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inputs {
inputs {
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value {
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producer_node_query {
id: "CsvExampleGen"
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min_count: 1
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}
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}
artifact_query {
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}
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"
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}
}
}
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
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}
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 {
}
}
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"
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state: LIVE
create_time_since_epoch: 1638699713343
last_update_time_since_epoch: 1638699713343
, artifact_type: id: 17
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)], 'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
properties {
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custom_properties {
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custom_properties {
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custom_properties {
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custom_properties {
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string_value: "FORMAT_TF_EXAMPLE"
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custom_properties {
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custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
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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"
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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 {
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custom_properties {
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value {
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, artifact_type: name: "ExampleStatistics"
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properties {
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custom_properties {
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custom_properties {
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value {
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, artifact_type: name: "Schema"
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custom_properties {
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value {
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, artifact_type: name: "ExampleAnomalies"
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properties {
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)], 'transform_graph': [Artifact(artifact: uri: "pipelines/penguin-transform/Transform/transform_graph/4"
custom_properties {
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value {
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}
, artifact_type: name: "TransformGraph"
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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 {
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id: "Transform"
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contexts {
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name: "pipeline"
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}
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"
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}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-transform.CsvExampleGen"
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}
}
artifact_query {
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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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}
}
}
context_queries {
type {
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}
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.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
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. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature sex has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature island has a shape dim {
size: 1
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. 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
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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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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
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state: LIVE
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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 {
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string_value: "penguin-transform:2021-12-05T10:21:51.187624:Trainer:model:0"
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, artifact_type: name: "Model"
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custom_properties {
key: "tfx_version"
value {
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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 {
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name: "tfx.components.pusher.component.Pusher"
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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
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execution_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 {
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value {
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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
"INFO:absl:Component İtici tamamlandı" ifadesini görmelisiniz. boru hattı başarıyla tamamlandıysa.
İtici bileşeni için eğitilmiş modeli iter SERVING_MODEL_DIR olan serving_model/penguin-transform önceki adımlarda değişkenleri değişmedi eğer dizin. Dosya tarayıcısının sonucunu Colab'de sol taraftaki panelde veya aşağıdaki komutu kullanarak görebilirsiniz:
# 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
Ayrıca kullanılarak oluşturulan modelin imzayı kontrol edebilirsiniz saved_model_cli aracı .
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
Belirlediğimiz Çünkü serving_default kendi ile serve_tf_examples_fn fonksiyonu, tek bir dize alır imza gösterileri. Bu dize tf.Examples bir tefrika dizedir ile ayrıştırılır tf.io.parse_example () daha önce açıklandığı gibi (daha tf.Examples hakkında bilgi fonksiyonu burada ).
Dışa aktarılan modeli yükleyebilir ve birkaç örnekle bazı çıkarımlar deneyebiliriz.
# 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]]
'Gentoo' türüne karşılık gelen üçüncü elementin, üçü arasında en büyüğü olması bekleniyor.
Sonraki adımlar
Daha yaklaşık bileşen Transform öğrenmek istiyorsanız, bkz Bileşen kılavuzu Transform . Üzerinde daha fazla kaynak bulabilirsiniz https://www.tensorflow.org/tfx/tutorials
Bakınız TFX Boru hatları anlama Tfx çeşitli kavramlar hakkında daha fazla bilgi edinmek.