Chuyển đổi dữ liệu đầu vào và theo dõi mô hình bằng đường dẫn TFX.
Xem trên TensorFlow.org
Chạy trong Google Colab
Xem nguồn trên GitHub
Tải xuống sổ ghi chépTrong hướng dẫn dựa trên sổ tay này, chúng tôi sẽ tạo và chạy một đường dẫn TFX để nhập dữ liệu đầu vào thô và xử lý trước nó một cách thích hợp cho việc đào tạo ML. Máy tính xách tay này được dựa trên các đường ống dẫn TFX chúng tôi xây dựng trong xác nhận dữ liệu sử dụng TFX đường ống và TensorFlow Data Validation Tutorial . Nếu bạn chưa đọc cuốn sổ đó, bạn nên đọc nó trước khi tiếp tục với cuốn sổ này.
Bạn có thể tăng chất lượng dự đoán của dữ liệu và / hoặc giảm kích thước bằng kỹ thuật tính năng. Một trong những lợi ích của việc sử dụng TFX là bạn sẽ viết mã chuyển đổi của mình một lần và các chuyển đổi kết quả sẽ nhất quán giữa đào tạo và phục vụ để tránh lệch đào tạo / phục vụ.
Chúng tôi sẽ bổ sung thêm một Transform thành phần để các đường ống dẫn. Các thành phần chuyển đổi được thực hiện bằng cách sử dụng tf.transform thư viện.
Xin vui lòng xem Hiểu TFX Đường ống để tìm hiểu thêm về các khái niệm khác nhau trong TFX.
Cài đặt
Trước tiên, chúng tôi cần cài đặt gói TFX Python và tải xuống tập dữ liệu mà chúng tôi sẽ sử dụng cho mô hình của mình.
Nâng cấp Pip
Để tránh nâng cấp Pip trong hệ thống khi chạy cục bộ, hãy kiểm tra để đảm bảo rằng chúng tôi đang chạy trong Colab. Hệ thống cục bộ tất nhiên có thể được nâng cấp riêng.
try:
import colab
!pip install --upgrade pip
except:
pass
Cài đặt TFX
pip install -U tfx
Bạn có khởi động lại thời gian chạy không?
Nếu bạn đang sử dụng Google Colab, lần đầu tiên bạn chạy ô ở trên, bạn phải khởi động lại thời gian chạy bằng cách nhấp vào phía trên nút "RESTART RUNTIME" hoặc sử dụng menu "Runtime> Restart runtime ...". Điều này là do cách Colab tải các gói.
Kiểm tra phiên bản TensorFlow và TFX.
import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.6.2 TFX version: 1.4.0
Thiết lập các biến
Có một số biến được sử dụng để xác định một đường ống. Bạn có thể tùy chỉnh các biến này theo ý muốn. Theo mặc định, tất cả đầu ra từ đường ống sẽ được tạo trong thư mục hiện tại.
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.
Chuẩn bị dữ liệu mẫu
Chúng tôi sẽ tải xuống tập dữ liệu mẫu để sử dụng trong đường dẫn TFX của chúng tôi. Bộ dữ liệu chúng tôi đang sử dụng là Palmer Penguins bộ dữ liệu .
Tuy nhiên, không giống như hướng dẫn trước đây đã sử dụng một bộ dữ liệu đã được xử lý trước, chúng tôi sẽ sử dụng Palmer Penguins bộ dữ liệu thô.
Vì thành phần TFX ExampleGen đọc đầu vào từ một thư mục, chúng ta cần tạo một thư mục và sao chép tập dữ liệu vào đó.
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>)
Hãy xem nhanh dữ liệu thô trông như thế nào.
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
Có một số mục với các giá trị được biểu diễn dưới dạng thiếu NA . Chúng tôi sẽ chỉ xóa các mục nhập đó trong hướng dẫn này.
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
Bạn sẽ có thể thấy bảy đặc điểm mô tả chim cánh cụt. Chúng tôi sẽ sử dụng bộ tính năng tương tự như các hướng dẫn trước - 'culmen_length_mm', 'culmen_depth_mm', 'flip_length_mm', 'body_mass_g' - và sẽ dự đoán 'loài' của chim cánh cụt.
Sự khác biệt duy nhất sẽ là dữ liệu đầu vào không được xử lý trước. Lưu ý rằng chúng tôi sẽ không sử dụng các tính năng khác như 'đảo' hoặc 'tình dục' trong hướng dẫn này.
Chuẩn bị một tệp giản đồ
Như đã trình bày trong xác nhận dữ liệu sử dụng TFX đường ống và TensorFlow Data Validation Hướng dẫn , chúng ta cần một file schema cho tập dữ liệu. Bởi vì tập dữ liệu khác với hướng dẫn trước, chúng tôi cần tạo lại nó. Trong hướng dẫn này, chúng tôi sẽ bỏ qua các bước đó và chỉ sử dụng tệp lược đồ đã chuẩn bị.
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>)
Tệp lược đồ này được tạo với cùng một đường dẫn như trong hướng dẫn trước mà không có bất kỳ thay đổi thủ công nào.
Tạo một đường dẫn
Các đường ống TFX được xác định bằng cách sử dụng các API Python. Chúng tôi sẽ thêm vào các Transform thành phần để các đường ống dẫn chúng ta tạo ra trong hướng dẫn Data Validation .
Một thành phần chuyển đổi đòi hỏi dữ liệu đầu vào từ một ExampleGen thành phần và một giản đồ từ một SchemaGen thành phần, và tạo ra một "chuyển đổi đồ thị". Kết quả sẽ được sử dụng trong một Trainer thành phần. Ngoài ra, Transform có thể tùy chọn tạo ra "dữ liệu đã được chuyển đổi", là dữ liệu được vật chất hóa sau khi chuyển đổi. Tuy nhiên, chúng tôi sẽ chuyển đổi dữ liệu trong quá trình đào tạo trong hướng dẫn này mà không cần cụ thể hóa dữ liệu đã chuyển đổi trung gian.
Một điều cần lưu ý là chúng ta cần phải xác định một hàm Python, preprocessing_fn để mô tả cách dữ liệu đầu vào nên được biến đổi. Điều này tương tự với thành phần Trainer cũng yêu cầu mã người dùng để định nghĩa mô hình.
Viết mã tiền xử lý và đào tạo
Chúng ta cần xác định hai hàm Python. Một cho Transform và một cho Trainer.
prerocessing_fn
Các thành phần chuyển đổi sẽ tìm thấy một hàm có tên preprocessing_fn trong file mô-đun cho là chúng tôi đã làm cho Trainer thành phần. Bạn cũng có thể chỉ định một chức năng cụ thể bằng cách sử dụng preprocessing_fn tham số của các thành phần Transform.
Trong ví dụ này, chúng ta sẽ thực hiện hai kiểu chuyển đổi. Đối với tính năng số liên tục như culmen_length_mm và body_mass_g , chúng tôi sẽ bình thường hóa những giá trị này bằng cách sử dụng tft.scale_to_z_score chức năng. Đối với tính năng nhãn, chúng ta cần chuyển đổi các nhãn chuỗi thành các giá trị chỉ mục số. Chúng tôi sẽ sử dụng tf.lookup.StaticHashTable để chuyển đổi.
Để xác định các lĩnh vực biến đổi một cách dễ dàng, chúng tôi thêm một _xf hậu tố với tên tính năng chuyển đổi.
run_fn
Bản thân mô hình gần giống như trong các bài hướng dẫn trước, nhưng lần này chúng ta sẽ biến đổi dữ liệu đầu vào bằng cách sử dụng đồ thị biến đổi từ thành phần Transform.
Một điểm khác biệt quan trọng hơn so với hướng dẫn trước là bây giờ chúng tôi xuất mô hình để phân phát, mô hình này không chỉ bao gồm biểu đồ tính toán của mô hình mà còn cả biểu đồ biến đổi để xử lý trước, được tạo trong thành phần Chuyển đổi. Chúng ta cần xác định một chức năng riêng biệt sẽ được sử dụng để phục vụ các yêu cầu đến. Bạn có thể thấy rằng các chức năng tương tự _apply_preprocessing được sử dụng cho cả các dữ liệu đào tạo và yêu cầu phục vụ.
_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
Bây giờ bạn đã hoàn thành tất cả các bước chuẩn bị để xây dựng đường ống TFX.
Viết định nghĩa đường ống
Chúng tôi xác định một chức năng để tạo một đường dẫn TFX. Một Pipeline đối tượng đại diện cho một đường ống TFX, có thể được chạy bằng một trong những hệ thống đường ống dàn nhạc mà TFX hỗ trợ.
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)
Chạy đường ống
Chúng tôi sẽ sử dụng LocalDagRunner như trong hướng dẫn trước.
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 {
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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 {
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id: "StatisticsGen"
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name: "node"
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name {
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string_value: "penguin-transform.StatisticsGen"
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artifact_query {
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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"
}
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custom_properties {
key: "tfx_version"
value {
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, 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"
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contexts {
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outputs {
outputs {
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value {
artifact_spec {
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properties {
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outputs {
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value {
artifact_spec {
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outputs {
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outputs {
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artifact_spec {
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parameters {
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upstream_nodes: "CsvExampleGen"
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execution_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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contexts {
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inputs {
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context_queries {
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context_queries {
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context_queries {
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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 {
name: "Schema"
}
}
}
}
outputs {
key: "post_transform_stats"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
}
}
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}
outputs {
key: "pre_transform_schema"
value {
artifact_spec {
type {
name: "Schema"
}
}
}
}
outputs {
key: "pre_transform_stats"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
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}
}
}
}
}
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"
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}
parameters {
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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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custom_properties {
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, artifact_type: name: "Schema"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:11.698540 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:22:11.707963 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Transform is finished.
INFO:absl:Component ExampleValidator is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.example_validator.component.ExampleValidator"
}
id: "ExampleValidator"
}
contexts {
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outputs {
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value {
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INFO:absl:MetadataStore with DB connection initialized
I1205 10:22:11.732254 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 5
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
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state: LIVE
create_time_since_epoch: 1638699713343
last_update_time_since_epoch: 1638699713343
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create_time_since_epoch: 1638699716011
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, artifact_type: id: 19
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outputs {
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value {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to pipelines/penguin-transform/ExampleValidator/anomalies/5/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to pipelines/penguin-transform/ExampleValidator/anomalies/5/Split-eval.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 5 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-transform/ExampleValidator/anomalies/5"
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properties {
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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
type_id: 15
uri: "pipelines/penguin-transform/CsvExampleGen/examples/1"
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execution_options {
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, pipeline_info=id: "penguin-transform"
, pipeline_run_id='2021-12-05T10:21:51.187624')
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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value {
string_value: "penguin-transform:2021-12-05T10:21:51.187624:Trainer:model:0"
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custom_properties {
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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 {
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custom_properties {
key: "tfx_version"
value {
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, artifact_type: name: "ModelRun"
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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"
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id: "Pusher"
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contexts {
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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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contexts {
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contexts {
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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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custom_properties {
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, artifact_type: name: "PushedModel"
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Pusher is finished.
I1205 10:22:18.092860 24712 rdbms_metadata_access_object.cc:686] No property is defined for the Type
Bạn sẽ thấy "INFO: absl: Component Pusher đã hoàn thành." nếu đường ống kết thúc thành công.
Thành phần đẩy đẩy mô hình đào tạo để các SERVING_MODEL_DIR là serving_model/penguin-transform thư mục nếu bạn không thay đổi các biến trong các bước trước. Bạn có thể xem kết quả từ trình duyệt tệp trong bảng điều khiển bên trái trong Colab hoặc sử dụng lệnh sau:
# 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
Bạn cũng có thể kiểm tra chữ ký của mô hình được tạo ra bằng cách sử dụng saved_model_cli công cụ .
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
Bởi vì chúng ta định nghĩa serving_default với riêng của chúng tôi serve_tf_examples_fn chức năng, các chương trình chữ ký mà phải mất một chuỗi duy nhất. Chuỗi này là một chuỗi tuần tự của tf.Examples và sẽ được phân tích với tf.io.parse_example () chức năng như chúng ta định nghĩa trước đó (tìm hiểu thêm về tf.Examples đây ).
Chúng tôi có thể tải mô hình đã xuất và thử một số suy luận với một vài ví dụ.
# 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]]
Nguyên tố thứ ba, tương ứng với loài 'Gentoo', được cho là lớn nhất trong số ba loài.
Bước tiếp theo
Nếu bạn muốn tìm hiểu về Biến đổi nhiều thành phần, xem chuyển đổi phần hướng dẫn . Bạn có thể tìm thêm các nguồn lực trên https://www.tensorflow.org/tfx/tutorials
Xin vui lòng xem Hiểu TFX Đường ống để tìm hiểu thêm về các khái niệm khác nhau trong TFX.