عرض على TensorFlow.org
تشغيل في Google Colab
عرض المصدر على جيثب
تحميل دفترفي هذا البرنامج التعليمي المستند إلى الكمبيوتر الدفتري ، سننشئ ونشغل خط أنابيب TFX الذي ينشئ نموذج تصنيف بسيطًا ويحلل أدائه عبر عمليات تشغيل متعددة. ويستند هذا الكمبيوتر الدفتري على خط أنابيب TFX بنينا في بسيط TFX خط أنابيب التعليمي . إذا لم تكن قد قرأت هذا البرنامج التعليمي حتى الآن ، فيجب عليك قراءته قبل المتابعة مع دفتر الملاحظات هذا.
أثناء تعديل نموذجك أو تدريبه باستخدام مجموعة بيانات جديدة ، تحتاج إلى التحقق مما إذا كان نموذجك قد تحسن أو أصبح أسوأ. قد لا يكون مجرد التحقق من مقاييس المستوى الأعلى مثل الدقة كافياً. يجب تقييم كل نموذج مدرب قبل دفعه إلى الإنتاج.
سنقوم بإضافة Evaluator مكون إلى خط أنابيب تم إنشاؤها في البرنامج التعليمي السابق. يُجري مكون المُقيِّم تحليلاً عميقًا لنماذجك ويقارن النموذج الجديد مقابل خط الأساس لتحديد أنها "جيدة بما يكفي". ويتم تنفيذ ذلك باستخدام TensorFlow تحليل نموذج المكتبة.
يرجى الاطلاع على فهم TFX خطوط الأنابيب لمعرفة المزيد عن مفاهيم مختلفة في TFX.
يثبت
عملية الإعداد هي نفس عملية البرنامج التعليمي السابق.
نحتاج أولاً إلى تثبيت حزمة TFX Python وتنزيل مجموعة البيانات التي سنستخدمها لنموذجنا.
ترقية النقطة
لتجنب ترقية Pip في نظام عند التشغيل محليًا ، تحقق للتأكد من أننا نعمل في Colab. يمكن بالطبع ترقية الأنظمة المحلية بشكل منفصل.
try:
import colab
!pip install --upgrade pip
except:
pass
قم بتثبيت TFX
pip install -U tfx
هل أعدت تشغيل وقت التشغيل؟
إذا كنت تستخدم Google Colab ، في المرة الأولى التي تقوم فيها بتشغيل الخلية أعلاه ، يجب إعادة تشغيل وقت التشغيل بالنقر فوق الزر "RESTART RUNTIME" أعلاه أو باستخدام قائمة "Runtime> Restart runtime ...". هذا بسبب الطريقة التي يقوم بها كولاب بتحميل الحزم.
تحقق من إصدارات TensorFlow و 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
قم بإعداد المتغيرات
هناك بعض المتغيرات المستخدمة لتحديد خط الأنابيب. يمكنك تخصيص هذه المتغيرات كما تريد. بشكل افتراضي ، سيتم إنشاء كل الإخراج من خط الأنابيب ضمن الدليل الحالي.
import os
PIPELINE_NAME = "penguin-tfma"
# 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.
تحضير البيانات النموذجية
سوف نستخدم نفس بالمر البطاريق مجموعة البيانات .
توجد أربع سمات رقمية في مجموعة البيانات هذه والتي تم تسويتها بالفعل ليكون لها نطاق [0،1]. سوف نبني نموذج التصنيف الذي يتنبأ species من طيور البطريق.
نظرًا لأن TFX ExampleGen تقرأ المدخلات من دليل ، نحتاج إلى إنشاء دليل ونسخ مجموعة البيانات إليه.
import urllib.request
import tempfile
DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_url, _data_filepath)
('/tmp/tfx-datal5lxy_yw/data.csv', <http.client.HTTPMessage at 0x7fa18a9da150>)
قم بإنشاء خط أنابيب
سنقوم بإضافة Evaluator مكون إلى خط أنابيب أنشأنا في بسيط TFX خط أنابيب التعليمي .
يتطلب عنصر مقيم إدخال البيانات من ExampleGen عنصر ونموذج من Trainer عنصر و tfma.EvalConfig الكائن. يمكننا اختياريًا توفير نموذج أساسي يمكن استخدامه لمقارنة المقاييس بالنموذج المدرب حديثًا.
مقيم يخلق نوعين من القطع الأثرية الإخراج، ModelEvaluation و ModelBlessing . يحتوي ModelEvaluation على نتيجة التقييم التفصيلية التي يمكن فحصها وتصورها بشكل أكبر باستخدام مكتبة TFMA. يحتوي ModelBlessing على نتيجة منطقية سواء اجتاز النموذج معايير معينة ويمكن استخدامه في مكونات لاحقة مثل أداة الدفع كإشارة.
اكتب كود التدريب النموذجي
سوف نستخدم نفس رمز نموذج كما هو الحال في بسيط TFX خط أنابيب التعليمي .
_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple
from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
from tensorflow_metadata.proto.v0 import schema_pb2
_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
# Since we're not generating or creating a schema, we will instead create
# a feature spec. Since there are a fairly small number of features this is
# manageable for this dataset.
_FEATURE_SPEC = {
**{
feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)
for feature in _FEATURE_KEYS
},
_LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)
}
def _input_fn(file_pattern: List[str],
data_accessor: DataAccessor,
schema: schema_pb2.Schema,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
schema: schema of the input data.
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.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_LABEL_KEY),
schema=schema).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=f) for f in _FEATURE_KEYS]
d = keras.layers.concatenate(inputs)
for _ in range(2):
d = keras.layers.Dense(8, activation='relu')(d)
outputs = keras.layers.Dense(3)(d)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=keras.optimizers.Adam(1e-2),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.summary(print_fn=logging.info)
return model
# TFX Trainer will call this function.
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
# This schema is usually either an output of SchemaGen or a manually-curated
# version provided by pipeline author. A schema can also derived from TFT
# graph if a Transform component is used. In the case when either is missing,
# `schema_from_feature_spec` could be used to generate schema from very simple
# feature_spec, but the schema returned would be very primitive.
schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
schema,
batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
schema,
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)
# The result of the training should be saved in `fn_args.serving_model_dir`
# directory.
model.save(fn_args.serving_model_dir, save_format='tf')
Writing penguin_trainer.py
اكتب تعريف خط الأنابيب
سنحدد وظيفة لإنشاء خط أنابيب TFX. بالإضافة إلى عنصر مقيم ذكرنا أعلاه، سوف نضيف واحد العقدة المزيد من دعا Resolver . للتحقق من أن النموذج الجديد يتحسن بشكل أفضل من النموذج السابق ، نحتاج إلى مقارنته بالنموذج السابق المنشور ، المسمى baseline. ML الفوقية (MLMD) المسارات كافة الأعمال الفنية السابقة للخطوط الأنابيب و Resolver أن تجد ما هو أحدث طراز المبارك - أقر نموذج مقيم بنجاح - من MLMD باستخدام فئة استراتيجية تدعى LatestBlessedModelStrategy .
import tensorflow_model_analysis as tfma
def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
module_file: str, serving_model_dir: str,
metadata_path: str) -> tfx.dsl.Pipeline:
"""Creates a three component penguin pipeline with TFX."""
# Brings data into the pipeline.
example_gen = tfx.components.CsvExampleGen(input_base=data_root)
# Uses user-provided Python function that trains a model.
trainer = tfx.components.Trainer(
module_file=module_file,
examples=example_gen.outputs['examples'],
train_args=tfx.proto.TrainArgs(num_steps=100),
eval_args=tfx.proto.EvalArgs(num_steps=5))
# NEW: Get the latest blessed model for Evaluator.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
# NEW: Uses TFMA to compute evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Calculate metrics for each penguin species.
tfma.SlicingSpec(feature_keys=['species']),
],
metrics_specs=[
tfma.MetricsSpec(per_slice_thresholds={
'sparse_categorical_accuracy':
tfma.PerSliceMetricThresholds(thresholds=[
tfma.PerSliceMetricThreshold(
slicing_specs=[tfma.SlicingSpec()],
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
)]),
})],
)
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'], # Pass an evaluation result.
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir)))
components = [
example_gen,
trainer,
# Following two components were added to the pipeline.
model_resolver,
evaluator,
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)
نحن بحاجة إلى توفير المعلومات التالية إلى مقيم عبر eval_config :
- مقاييس إضافية لتكوينها (إذا كنت تريد مقاييس أكثر مما هو محدد في النموذج).
- شرائح للتكوين
- عتبات التحقق من صحة النموذج للتحقق مما إذا كان سيتم تضمين التحقق
لأن SparseCategoricalAccuracy أدرج بالفعل في model.compile() المكالمة، فإنه سيتم تضمينها في التحليل تلقائيا. لذلك نحن لا نضيف أي مقاييس إضافية هنا. SparseCategoricalAccuracy سيتم استخدامها لتقرر ما إذا كان النموذج هو ما يكفي جيدة أيضا.
نحسب المقاييس لمجموعة البيانات بأكملها ولكل نوع من أنواع البطريق. SlicingSpec يحدد كيفية تجميع المقاييس المعلنة.
هناك عتبتان يجب أن يجتازهما النموذج الجديد ، أحدهما حد مطلق قدره 0.6 والآخر هو حد نسبي يجب أن يكون أعلى من النموذج الأساسي. عند تشغيل خط أنابيب للمرة الأولى، و change_threshold سيتم تجاهل وسيتم فحص فقط value_threshold. إذا قمت بتشغيل خط الأنابيب أكثر من مرة، و Resolver سوف تجد نموذجا من تشغيل السابقة وسيتم استخدامها كنموذج أساسي للمقارنة.
انظر دليل مكون مقيم لمزيد من المعلومات.
قم بتشغيل خط الأنابيب
سوف نستخدم LocalDagRunner كما في السابق تعليمي.
tfx.orchestration.LocalDagRunner().run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
module_file=_trainer_module_file,
serving_model_dir=SERVING_MODEL_DIR,
metadata_path=METADATA_PATH))
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_trainer.py' (including modules: ['penguin_trainer']).
INFO:absl:User module package has hash fingerprint version 1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpr3anh67s/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp6s2sw4dj', '--dist-dir', '/tmp/tmp6jr76e54']
/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-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-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: "Evaluator"
value {
beam_executable_spec {
python_executor_spec {
class_path: "tfx.components.evaluator.executor.Executor"
}
}
}
}
executor_specs {
key: "Pusher"
value {
python_class_executable_spec {
class_path: "tfx.components.pusher.executor.Executor"
}
}
}
executor_specs {
key: "Trainer"
value {
python_class_executable_spec {
class_path: "tfx.components.trainer.executor.GenericExecutor"
}
}
}
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-tfma/metadata.db"
connection_mode: READWRITE_OPENCREATE
}
}
INFO:absl:Using connection config:
sqlite {
filename_uri: "metadata/penguin-tfma/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-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.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-datal5lxy_yw"
}
}
}
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: "Evaluator"
downstream_nodes: "Trainer"
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_trainer.py -> build/lib
installing to /tmp/tmp6s2sw4dj
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmp6s2sw4dj
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/tmp6s2sw4dj/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3.7.egg-info
running install_scripts
creating /tmp/tmp6s2sw4dj/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL
creating '/tmp/tmp6jr76e54/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' and adding '/tmp/tmp6s2sw4dj' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/RECORD'
removing /tmp/tmp6s2sw4dj
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 10:34:23.723806 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.730262 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.736788 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.744907 28099 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
I1205 10:34:23.758380 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 1
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
custom_properties {
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)]}), exec_properties={'output_file_format': 5, '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_data_format': 6, 'input_base': '/tmp/tfx-datal5lxy_yw', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638700463,sum_checksum:1638700463'}, execution_output_uri='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfma/CsvExampleGen/.system/stateful_working_dir/2021-12-05T10:34:23.517028', tmp_dir='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
type {
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id: "CsvExampleGen"
}
contexts {
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contexts {
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name {
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outputs {
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value {
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name: "Examples"
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execution_options {
caching_options {
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, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
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-datal5lxy_yw/* 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-tfma/CsvExampleGen/examples/1"
custom_properties {
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properties {
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)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component latest_blessed_model_resolver is running.
INFO:absl:Running launcher for node_info {
type {
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INFO:absl:Running as an resolver node.
INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:Artifact type Model is not found in MLMD.
WARNING:absl:Artifact type ModelBlessing is not found in MLMD.
I1205 10:34:24.899447 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component latest_blessed_model_resolver is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.trainer.component.Trainer"
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id: "Trainer"
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contexts {
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upstream_nodes: "CsvExampleGen"
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:24.924589 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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-tfma/CsvExampleGen/examples/1"
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create_time_since_epoch: 1638700464882
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inputs {
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context_queries {
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artifact_query {
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outputs {
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upstream_nodes: "CsvExampleGen"
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execution_options {
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, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
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 {'train_args': '{\n "num_steps": 100\n}', 'custom_config': 'null', 'eval_args': '{\n "num_steps": 5\n}', 'module_path': 'penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpc97ini82', 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl']
Processing ./pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-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 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+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703
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 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 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 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 3ms/step - loss: 0.5273 - sparse_categorical_accuracy: 0.8175 - val_loss: 0.2412 - val_sparse_categorical_accuracy: 0.9600
2021-12-05 10:34:29.879208: 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-tfma/Trainer/model/3/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-tfma/Trainer/model/3/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-tfma/Trainer/model/3/Format-Serving. ModelRun written to pipelines/penguin-tfma/Trainer/model_run/3
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'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfma/Trainer/model_run/3"
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INFO:absl:MetadataStore with DB connection initialized
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I1205 10:34:30.404250 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Trainer is finished.
INFO:absl:Component Evaluator is running.
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INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:30.428037 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 4
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={'examples': [Artifact(artifact: id: 1
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, pipeline_info=id: "penguin-tfma"
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INFO:absl:udf_utils.get_fn {'example_splits': 'null', 'eval_config': '{\n "metrics_specs": [\n {\n "per_slice_thresholds": {\n "sparse_categorical_accuracy": {\n "thresholds": [\n {\n "slicing_specs": [\n {}\n ],\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.6\n }\n }\n }\n ]\n }\n }\n }\n ],\n "model_specs": [\n {\n "label_key": "species"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "species"\n ]\n }\n ]\n}', 'fairness_indicator_thresholds': 'null'} 'custom_eval_shared_model'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
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INFO:absl:Using pipelines/penguin-tfma/Trainer/model/3/Format-Serving as model.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'example_splits': 'null', 'eval_config': '{\n "metrics_specs": [\n {\n "per_slice_thresholds": {\n "sparse_categorical_accuracy": {\n "thresholds": [\n {\n "slicing_specs": [\n {}\n ],\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.6\n }\n }\n }\n ]\n }\n }\n }\n ],\n "model_specs": [\n {\n "label_key": "species"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "species"\n ]\n }\n ]\n}', 'fairness_indicator_thresholds': 'null'} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
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slicing_specs {
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slicing_specs {
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INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
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slicing_specs {
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INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
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slicing_specs {
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slicing_specs {
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WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Evaluation complete. Results written to pipelines/penguin-tfma/Evaluator/evaluation/4.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:114: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:114: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to pipelines/penguin-tfma/Evaluator/blessing/4.
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'>, {'blessing': [Artifact(artifact: uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
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INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.040588 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:35.045548 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Evaluator is finished.
INFO:absl:Component Pusher is running.
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}
}
upstream_nodes: "Evaluator"
upstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.068168 28099 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={'model': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Trainer:model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
create_time_since_epoch: 1638700470409
last_update_time_since_epoch: 1638700470409
, artifact_type: id: 19
name: "Model"
)], 'model_blessing': [Artifact(artifact: id: 4
type_id: 21
uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
custom_properties {
key: "blessed"
value {
int_value: 1
}
}
custom_properties {
key: "current_model"
value {
string_value: "pipelines/penguin-tfma/Trainer/model/3"
}
}
custom_properties {
key: "current_model_id"
value {
int_value: 3
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Evaluator:blessing:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
create_time_since_epoch: 1638700475049
last_update_time_since_epoch: 1638700475049
, artifact_type: id: 21
name: "ModelBlessing"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Pusher/pushed_model/5"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Pusher:pushed_model:0"
}
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'custom_config': 'null', 'push_destination': '{\n "filesystem": {\n "base_directory": "serving_model/penguin-tfma"\n }\n}'}, execution_output_uri='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/executor_output.pb', stateful_working_dir='pipelines/penguin-tfma/Pusher/.system/stateful_working_dir/2021-12-05T10:34:23.517028', tmp_dir='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.pusher.component.Pusher"
}
id: "Pusher"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Pusher"
}
}
}
}
inputs {
inputs {
key: "model"
value {
channels {
producer_node_query {
id: "Trainer"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Trainer"
}
}
}
artifact_query {
type {
name: "Model"
}
}
output_key: "model"
}
}
}
inputs {
key: "model_blessing"
value {
channels {
producer_node_query {
id: "Evaluator"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Evaluator"
}
}
}
artifact_query {
type {
name: "ModelBlessing"
}
}
output_key: "blessing"
}
}
}
}
outputs {
outputs {
key: "pushed_model"
value {
artifact_spec {
type {
name: "PushedModel"
}
}
}
}
}
parameters {
parameters {
key: "custom_config"
value {
field_value {
string_value: "null"
}
}
}
parameters {
key: "push_destination"
value {
field_value {
string_value: "{\n \"filesystem\": {\n \"base_directory\": \"serving_model/penguin-tfma\"\n }\n}"
}
}
}
}
upstream_nodes: "Evaluator"
upstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
INFO:absl:Model version: 1638700475
INFO:absl:Model written to serving path serving_model/penguin-tfma/1638700475.
INFO:absl:Model pushed to pipelines/penguin-tfma/Pusher/pushed_model/5.
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'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Pusher/pushed_model/5"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Pusher:pushed_model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "PushedModel"
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.098553 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is finished.
عند اكتمال خط الأنابيب ، يجب أن تكون قادرًا على رؤية شيء مثل التالي:
INFO:absl:Blessing result True written to pipelines/penguin-tfma/Evaluator/blessing/4.
أو يمكنك أيضًا التحقق يدويًا من دليل الإخراج حيث يتم تخزين القطع الأثرية التي تم إنشاؤها. إذا قمت بزيارة pipelines/penguin-tfma/Evaluator/blessing/ مع broswer على الملف، يمكنك ان ترى ملف باسم BLESSED أو NOT_BLESSED وفقا لنتيجة التقييم.
إذا كانت النتيجة نعمة هي False ، ومروج المخدرات يرفضون دفع النموذج إلى serving_model_dir ، لأن النموذج لا يكفي جيدة لاستخدامها في الإنتاج.
يمكنك تشغيل خط الأنابيب مرة أخرى ربما باستخدام تكوينات تقييم مختلفة. حتى إذا قمت بتشغيل خط أنابيب مع نفس التكوين الدقيق ورقة العمل، قد يكون نموذج تدريب مختلفة قليلا نظرا لعشوائية يتجزأ من التدريب النموذج الذي يمكن أن يؤدي إلى NOT_BLESSED نموذج.
افحص مخرجات خط الأنابيب
يمكنك استخدام TFMA للتحقيق في نتيجة التقييم وتصورها في الأداة ModelEvaluation.
الحصول على نتيجة التحليل من النتائج الأثرية
يمكنك استخدام واجهات برمجة تطبيقات MLMD لتحديد هذه المخرجات برمجياً. أولاً ، سنحدد بعض وظائف الأداة المساعدة للبحث عن عناصر الإخراج التي تم إنتاجها للتو.
from ml_metadata.proto import metadata_store_pb2
# Non-public APIs, just for showcase.
from tfx.orchestration.portable.mlmd import execution_lib
# TODO(b/171447278): Move these functions into the TFX library.
def get_latest_artifacts(metadata, pipeline_name, component_id):
"""Output artifacts of the latest run of the component."""
context = metadata.store.get_context_by_type_and_name(
'node', f'{pipeline_name}.{component_id}')
executions = metadata.store.get_executions_by_context(context.id)
latest_execution = max(executions,
key=lambda e:e.last_update_time_since_epoch)
return execution_lib.get_artifacts_dict(metadata, latest_execution.id,
[metadata_store_pb2.Event.OUTPUT])
يمكننا العثور على أحدث تنفيذ Evaluator عنصر والحصول على القطع الأثرية الناتج منه.
# Non-public APIs, just for showcase.
from tfx.orchestration.metadata import Metadata
from tfx.types import standard_component_specs
metadata_connection_config = tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH)
with Metadata(metadata_connection_config) as metadata_handler:
# Find output artifacts from MLMD.
evaluator_output = get_latest_artifacts(metadata_handler, PIPELINE_NAME,
'Evaluator')
eval_artifact = evaluator_output[standard_component_specs.EVALUATION_KEY][0]
INFO:absl:MetadataStore with DB connection initialized
Evaluator دوما بإرجاع القطع الأثرية تقييم واحد، ويمكننا تصور ذلك باستخدام مكتبة TensorFlow التحليل النموذجي. على سبيل المثال ، ستعرض الكود التالي مقاييس الدقة لكل نوع من أنواع البطريق.
import tensorflow_model_analysis as tfma
eval_result = tfma.load_eval_result(eval_artifact.uri)
tfma.view.render_slicing_metrics(eval_result, slicing_column='species')
SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'species:0', 'metrics…
إذا اخترت "sparse_categorical_accuracy" في Show القائمة المنسدلة، يمكنك ان ترى القيم دقة في الأنواع. قد ترغب في إضافة المزيد من الشرائح والتحقق مما إذا كان نموذجك مناسبًا لجميع التوزيعات وما إذا كان هناك أي تحيز محتمل.
الخطوات التالية
معرفة المزيد عن تحليل نموذج في TensorFlow تحليل نموذج مكتبة البرنامج التعليمي .
يمكنك العثور على مزيد من الموارد على https://www.tensorflow.org/tfx/tutorials
يرجى الاطلاع على فهم TFX خطوط الأنابيب لمعرفة المزيد عن مفاهيم مختلفة في TFX.