Un court tutoriel pour exécuter un pipeline TFX simple.
Dans ce didacticiel basé sur un bloc-notes, nous allons créer et exécuter un pipeline TFX pour un modèle de classification simple. Le pipeline comprendra trois composants TFX essentiels : ExampleGen, Trainer et Pusher. Le pipeline comprend le flux de travail ML le plus minimal, comme l'importation de données, la formation d'un modèle et l'exportation du modèle formé.
S'il vous plaît voir Comprendre TFX Pipelines pour en savoir plus sur les différents concepts TFX.
D'installation
Nous devons d'abord installer le package Python TFX et télécharger le jeu de données que nous utiliserons pour notre modèle.
Pip de mise à niveau
Pour éviter de mettre à niveau Pip dans un système lors de l'exécution locale, assurez-vous que nous exécutons dans Colab. Les systèmes locaux peuvent bien sûr être mis à niveau séparément.
try:
import colab
!pip install --upgrade pip
except:
pass
Installer TFX
pip install -U tfx
As-tu redémarré le runtime ?
Si vous utilisez Google Colab, la première fois que vous exécutez la cellule ci-dessus, vous devez redémarrer le runtime en cliquant au-dessus du bouton "RESTART RUNTIME" ou en utilisant le menu "Runtime> Restart runtime ...". Cela est dû à la façon dont Colab charge les packages.
Vérifiez les versions TensorFlow et 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
Configurer des variables
Certaines variables sont utilisées pour définir un pipeline. Vous pouvez personnaliser ces variables comme vous le souhaitez. Par défaut, toutes les sorties du pipeline seront générées sous le répertoire actuel.
import os
PIPELINE_NAME = "penguin-simple"
# 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.
Préparer des exemples de données
Nous allons télécharger l'exemple de jeu de données à utiliser dans notre pipeline TFX. L'ensemble de données que nous utilisons est dataset Palmer Penguins qui est également utilisé dans d' autres exemples de TFX .
Il y a quatre caractéristiques numériques dans cet ensemble de données :
- culmen_length_mm
- culmen_depth_mm
- longueur_nageoire_mm
- masse_corps_g
Toutes les caractéristiques étaient déjà normalisées pour avoir la plage [0,1]. Nous allons construire un modèle de classification qui prédit les species
de manchots.
Étant donné que TFX ExampleGen lit les entrées d'un répertoire, nous devons créer un répertoire et y copier l'ensemble de données.
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-dataijanq9u3/data.csv', <http.client.HTTPMessage at 0x7f487953d110>)
Jetez un coup d'œil au fichier CSV.
head {_data_filepath}
species,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g 0,0.2545454545454545,0.6666666666666666,0.15254237288135594,0.2916666666666667 0,0.26909090909090905,0.5119047619047618,0.23728813559322035,0.3055555555555556 0,0.29818181818181805,0.5833333333333334,0.3898305084745763,0.1527777777777778 0,0.16727272727272732,0.7380952380952381,0.3559322033898305,0.20833333333333334 0,0.26181818181818167,0.892857142857143,0.3050847457627119,0.2638888888888889 0,0.24727272727272717,0.5595238095238096,0.15254237288135594,0.2569444444444444 0,0.25818181818181823,0.773809523809524,0.3898305084745763,0.5486111111111112 0,0.32727272727272727,0.5357142857142859,0.1694915254237288,0.1388888888888889 0,0.23636363636363636,0.9642857142857142,0.3220338983050847,0.3055555555555556
Vous devriez pouvoir voir cinq valeurs. species
est l' un de 0, 1 ou 2, et toutes les autres fonctions doivent avoir des valeurs comprises entre 0 et 1.
Créer un pipeline
Les pipelines TFX sont définis à l'aide d'API Python. Nous allons définir un pipeline qui consiste à suivre trois composants.
- CsvExampleGen : lit les fichiers de données et les convertit au format interne TFX pour un traitement ultérieur. Il y a plusieurs ExampleGen s pour différents formats. Dans ce tutoriel, nous utiliserons CsvExampleGen qui prend l'entrée du fichier CSV.
- Formateur : entraîne un modèle ML. Composant formateur nécessite un code de définition du modèle des utilisateurs. Vous pouvez utiliser les API tensorflow pour spécifier comment former un modèle et l' enregistrer dans un format de modèle _saved.
- Pusher : copie le modèle entraîné en dehors du pipeline TFX. Élément pousseur peut être considéré d'un processus de déploiement du modèle ML formé.
Avant de définir réellement le pipeline, nous devons d'abord écrire un code de modèle pour le composant Trainer.
Écrire le code d'entraînement du modèle
Nous allons créer un modèle DNN simple pour la classification à l'aide de l'API TensorFlow Keras. Ce code d'entraînement modèle sera enregistré dans un fichier séparé.
Dans ce tutoriel , nous utiliserons Entraîneur générique de TFX qui soutiennent les modèles à base Keras. Vous devez écrire un fichier Python contenant run_fn
fonction, qui est le point d' entrée pour le Trainer
composant.
_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
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 import v1 as tfx
from tfx_bsl.public import tfxio
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: tfx.components.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,
tfxio.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: tfx.components.FnArgs):
"""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
Vous avez maintenant terminé toutes les étapes de préparation pour créer un pipeline TFX.
Écrire une définition de pipeline
Nous définissons une fonction pour créer un pipeline TFX. Un Pipeline
objet représente un pipeline de TFX qui peut être exécuté en utilisant l' un des systèmes d'orchestration de pipeline qui TFX supports.
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))
# 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)))
# Following three components will be included in the pipeline.
components = [
example_gen,
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)
Exécuter le pipeline
TFX prend en charge plusieurs orchestrateurs pour exécuter des pipelines. Dans ce tutoriel , nous utiliserons LocalDagRunner
qui est inclus dans les pipelines de paquet Python TFX et fonctionne sur l' environnement local. Nous appelons souvent les pipelines TFX « DAG », ce qui signifie graphe acyclique dirigé.
LocalDagRunner
fournit des itérations rapides pour developemnt et le débogage. TFX prend également en charge d'autres orchestrateurs, notamment Kubeflow Pipelines et Apache Airflow, qui conviennent aux cas d'utilisation en production.
Voir TFX sur le Cloud AI Platform Pipelines ou TFX Airflow Tutoriel pour en savoir plus sur d' autres systèmes d'orchestration.
Maintenant , nous créons un LocalDagRunner
et passer un Pipeline
objet créé à partir de la fonction que nous avons déjà défini.
Le pipeline s'exécute directement et vous pouvez voir les journaux de la progression du pipeline, y compris l'entraînement du modèle ML.
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 a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp28n_co8j/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpfb02sbta', '--dist-dir', '/tmp/tmpyu7gi15_'] /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-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl'; target user module is 'penguin_trainer'. INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-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: "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-simple/metadata.db" connection_mode: READWRITE_OPENCREATE } } INFO:absl:Using connection config: sqlite { filename_uri: "metadata/penguin-simple/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-simple" } } } contexts { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } contexts { type { name: "node" } name { field_value { string_value: "penguin-simple.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-dataijanq9u3" } } } 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: "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/tmpfb02sbta running install running install_lib copying build/lib/penguin_trainer.py -> /tmp/tmpfb02sbta 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/tmpfb02sbta/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3.7.egg-info running install_scripts creating /tmp/tmpfb02sbta/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/WHEEL creating '/tmp/tmpyu7gi15_/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl' and adding '/tmp/tmpfb02sbta' to it adding 'penguin_trainer.py' adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/METADATA' adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/WHEEL' adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/top_level.txt' adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/RECORD' removing /tmp/tmpfb02sbta WARNING: Logging before InitGoogleLogging() is written to STDERR I1205 10:44:07.061197 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type I1205 10:44:07.067816 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type I1205 10:44:07.074599 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type I1205 10:44:07.081624 30480 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 10:44:07.136307 30480 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-simple/CsvExampleGen/examples/1" custom_properties { key: "input_fingerprint" value { string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638701046,sum_checksum:1638701046" } } custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974: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_base': '/tmp/tfx-dataijanq9u3', 'input_config': '{\n "splits": [\n {\n "name": "single_split",\n "pattern": "*"\n }\n ]\n}', 'output_file_format': 5, 'output_data_format': 6, 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638701046,sum_checksum:1638701046'}, execution_output_uri='pipelines/penguin-simple/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-simple/CsvExampleGen/.system/stateful_working_dir/2021-12-05T10:44:06.706974', tmp_dir='pipelines/penguin-simple/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-simple" } } } contexts { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } contexts { type { name: "node" } name { field_value { string_value: "penguin-simple.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-dataijanq9u3" } } } 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: "Trainer" execution_options { caching_options { } } , pipeline_info=id: "penguin-simple" , pipeline_run_id='2021-12-05T10:44:06.706974') 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-dataijanq9u3/* 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-simple/CsvExampleGen/examples/1" custom_properties { key: "input_fingerprint" value { string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638701046,sum_checksum:1638701046" } } custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974: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 Trainer is running. INFO:absl:Running launcher for node_info { type { name: "tfx.components.trainer.component.Trainer" } id: "Trainer" } contexts { contexts { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } contexts { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } contexts { type { name: "node" } name { field_value { string_value: "penguin-simple.Trainer" } } } } inputs { inputs { key: "examples" value { channels { producer_node_query { id: "CsvExampleGen" } context_queries { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } context_queries { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } context_queries { type { name: "node" } name { field_value { string_value: "penguin-simple.CsvExampleGen" } } } artifact_query { type { name: "Examples" } } output_key: "examples" } min_count: 1 } } } outputs { outputs { key: "model" value { artifact_spec { type { name: "Model" } } } } outputs { key: "model_run" value { artifact_spec { type { name: "ModelRun" } } } } } parameters { parameters { key: "custom_config" value { field_value { string_value: "null" } } } parameters { key: "eval_args" value { field_value { string_value: "{\n \"num_steps\": 5\n}" } } } parameters { key: "module_path" value { field_value { string_value: "penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl" } } } parameters { key: "train_args" value { field_value { string_value: "{\n \"num_steps\": 100\n}" } } } } upstream_nodes: "CsvExampleGen" downstream_nodes: "Pusher" execution_options { caching_options { } } INFO:absl:MetadataStore with DB connection initialized INFO:absl:MetadataStore with DB connection initialized I1205 10:44:08.274386 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type INFO:absl:Going to run a new execution 2 INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=2, input_dict={'examples': [Artifact(artifact: id: 1 type_id: 15 uri: "pipelines/penguin-simple/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:25648,xor_checksum:1638701046,sum_checksum:1638701046" } } custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974: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: 1638701048257 last_update_time_since_epoch: 1638701048257 , 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'>, {'model': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model/2" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model:0" } } , artifact_type: name: "Model" )], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model_run/2" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model_run:0" } } , artifact_type: name: "ModelRun" )]}), exec_properties={'custom_config': 'null', 'module_path': 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl', 'train_args': '{\n "num_steps": 100\n}', 'eval_args': '{\n "num_steps": 5\n}'}, execution_output_uri='pipelines/penguin-simple/Trainer/.system/executor_execution/2/executor_output.pb', stateful_working_dir='pipelines/penguin-simple/Trainer/.system/stateful_working_dir/2021-12-05T10:44:06.706974', tmp_dir='pipelines/penguin-simple/Trainer/.system/executor_execution/2/.temp/', pipeline_node=node_info { type { name: "tfx.components.trainer.component.Trainer" } id: "Trainer" } contexts { contexts { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } contexts { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } contexts { type { name: "node" } name { field_value { string_value: "penguin-simple.Trainer" } } } } inputs { inputs { key: "examples" value { channels { producer_node_query { id: "CsvExampleGen" } context_queries { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } context_queries { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } context_queries { type { name: "node" } name { field_value { string_value: "penguin-simple.CsvExampleGen" } } } artifact_query { type { name: "Examples" } } output_key: "examples" } min_count: 1 } } } outputs { outputs { key: "model" value { artifact_spec { type { name: "Model" } } } } outputs { key: "model_run" value { artifact_spec { type { name: "ModelRun" } } } } } parameters { parameters { key: "custom_config" value { field_value { string_value: "null" } } } parameters { key: "eval_args" value { field_value { string_value: "{\n \"num_steps\": 5\n}" } } } parameters { key: "module_path" value { field_value { string_value: "penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl" } } } parameters { key: "train_args" value { field_value { string_value: "{\n \"num_steps\": 100\n}" } } } } upstream_nodes: "CsvExampleGen" downstream_nodes: "Pusher" execution_options { caching_options { } } , pipeline_info=id: "penguin-simple" , pipeline_run_id='2021-12-05T10:44:06.706974') 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', 'module_path': 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl', 'train_args': '{\n "num_steps": 100\n}', 'eval_args': '{\n "num_steps": 5\n}'} 'run_fn' INFO:absl:Installing 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp9yk6w_js', 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl'] Processing ./pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl INFO:absl:Successfully installed 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-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+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc 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: 0.4074 - sparse_categorical_accuracy: 0.8755 - val_loss: 0.0760 - val_sparse_categorical_accuracy: 0.9800 2021-12-05 10:44:13.263941: 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-simple/Trainer/model/2/Format-Serving/assets INFO:tensorflow:Assets written to: pipelines/penguin-simple/Trainer/model/2/Format-Serving/assets INFO:absl:Training complete. Model written to pipelines/penguin-simple/Trainer/model/2/Format-Serving. ModelRun written to pipelines/penguin-simple/Trainer/model_run/2 INFO:absl:Cleaning up stateless execution info. INFO:absl:Execution 2 succeeded. INFO:absl:Cleaning up stateful execution info. INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model/2" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model:0" } } custom_properties { key: "tfx_version" value { string_value: "1.4.0" } } , artifact_type: name: "Model" )], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model_run/2" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model_run:0" } } custom_properties { key: "tfx_version" value { string_value: "1.4.0" } } , artifact_type: name: "ModelRun" )]}) for execution 2 INFO:absl:MetadataStore with DB connection initialized INFO:absl:Component Trainer is finished. I1205 10:44:13.795414 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type INFO:absl:Component Pusher is running. I1205 10:44:13.799805 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type INFO:absl:Running launcher for node_info { type { name: "tfx.components.pusher.component.Pusher" } id: "Pusher" } contexts { contexts { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } contexts { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } contexts { type { name: "node" } name { field_value { string_value: "penguin-simple.Pusher" } } } } inputs { inputs { key: "model" value { channels { producer_node_query { id: "Trainer" } context_queries { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } context_queries { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } context_queries { type { name: "node" } name { field_value { string_value: "penguin-simple.Trainer" } } } artifact_query { type { name: "Model" } } output_key: "model" } } } } 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-simple\"\n }\n}" } } } } upstream_nodes: "Trainer" execution_options { caching_options { } } INFO:absl:MetadataStore with DB connection initialized I1205 10:44:13.821346 30480 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={'model': [Artifact(artifact: id: 2 type_id: 17 uri: "pipelines/penguin-simple/Trainer/model/2" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model:0" } } custom_properties { key: "tfx_version" value { string_value: "1.4.0" } } state: LIVE create_time_since_epoch: 1638701053803 last_update_time_since_epoch: 1638701053803 , artifact_type: id: 17 name: "Model" )]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-simple/Pusher/pushed_model/3" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Pusher:pushed_model:0" } } , artifact_type: name: "PushedModel" )]}), exec_properties={'push_destination': '{\n "filesystem": {\n "base_directory": "serving_model/penguin-simple"\n }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-simple/Pusher/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-simple/Pusher/.system/stateful_working_dir/2021-12-05T10:44:06.706974', tmp_dir='pipelines/penguin-simple/Pusher/.system/executor_execution/3/.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-simple" } } } contexts { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } contexts { type { name: "node" } name { field_value { string_value: "penguin-simple.Pusher" } } } } inputs { inputs { key: "model" value { channels { producer_node_query { id: "Trainer" } context_queries { type { name: "pipeline" } name { field_value { string_value: "penguin-simple" } } } context_queries { type { name: "pipeline_run" } name { field_value { string_value: "2021-12-05T10:44:06.706974" } } } context_queries { type { name: "node" } name { field_value { string_value: "penguin-simple.Trainer" } } } artifact_query { type { name: "Model" } } output_key: "model" } } } } 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-simple\"\n }\n}" } } } } upstream_nodes: "Trainer" execution_options { caching_options { } } , pipeline_info=id: "penguin-simple" , pipeline_run_id='2021-12-05T10:44:06.706974') WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline. INFO:absl:Model version: 1638701053 INFO:absl:Model written to serving path serving_model/penguin-simple/1638701053. INFO:absl:Model pushed to pipelines/penguin-simple/Pusher/pushed_model/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'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-simple/Pusher/pushed_model/3" custom_properties { key: "name" value { string_value: "penguin-simple:2021-12-05T10:44:06.706974:Pusher:pushed_model:0" } } custom_properties { key: "tfx_version" value { string_value: "1.4.0" } } , artifact_type: name: "PushedModel" )]}) for execution 3 INFO:absl:MetadataStore with DB connection initialized INFO:absl:Component Pusher is finished. I1205 10:44:13.851651 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
Vous devriez voir « INFO:absl:Component Pusher terminé. à la fin des journaux si le pipeline s'est terminé avec succès. Parce que Pusher
composant est le dernier composant du pipeline.
Le composant poussoir pousse le modèle formé à l' SERVING_MODEL_DIR
qui est le serving_model/penguin-simple
, répertoire si vous ne modifiez pas les variables dans les étapes précédentes. Vous pouvez voir le résultat à partir du navigateur de fichiers dans le panneau de gauche de Colab, ou à l'aide de la commande suivante :
# List files in created model directory.
find {SERVING_MODEL_DIR}
serving_model/penguin-simple serving_model/penguin-simple/1638701053 serving_model/penguin-simple/1638701053/keras_metadata.pb serving_model/penguin-simple/1638701053/assets serving_model/penguin-simple/1638701053/variables serving_model/penguin-simple/1638701053/variables/variables.data-00000-of-00001 serving_model/penguin-simple/1638701053/variables/variables.index serving_model/penguin-simple/1638701053/saved_model.pb
Prochaines étapes
Vous pouvez trouver plus de ressources sur https://www.tensorflow.org/tfx/tutorials
S'il vous plaît voir Comprendre TFX Pipelines pour en savoir plus sur les différents concepts TFX.