Ver en TensorFlow.org
Ejecutar en Google Colab
Ver fuente en GitHub
Descargar cuadernoEn este tutorial basado en notebook, crearemos y ejecutaremos canalizaciones TFX para validar los datos de entrada y crear un modelo ML. Este portátil se basa en la tubería TFX hemos construido en simple TFX Pipeline Tutorial . Si aún no ha leído ese tutorial, debe leerlo antes de continuar con este cuaderno.
La primera tarea en cualquier proyecto de ciencia de datos o ML es comprender y limpiar los datos, lo que incluye:
- Comprender los tipos de datos, las distribuciones y otra información (p. Ej., Valor medio o número de exclusivos) sobre cada característica.
- Generando un esquema preliminar que describe los datos
- Identificar anomalías y valores faltantes en los datos con respecto al esquema dado
En este tutorial, crearemos dos canalizaciones TFX.
Primero, crearemos una canalización para analizar el conjunto de datos y generar un esquema preliminar del conjunto de datos dado. Este gasoducto incluirá dos nuevos componentes, StatisticsGen y SchemaGen .
Una vez que tengamos un esquema adecuado de los datos, crearemos una canalización para entrenar un modelo de clasificación ML basado en la canalización del tutorial anterior. En este oleoducto, vamos a utilizar el esquema de la primera tubería y un nuevo componente, ExampleValidator , para validar los datos de entrada.
Los tres nuevos componentes, StatisticsGen, SchemaGen y ExampleValidator, son componentes TFX para el análisis de datos y validación, y se implementan utilizando el TensorFlow validación de datos de la biblioteca.
Por favor, vea Comprender TFX Tuberías para aprender más acerca de los diversos conceptos en TFX.
Configurar
Primero necesitamos instalar el paquete TFX Python y descargar el conjunto de datos que usaremos para nuestro modelo.
Actualizar Pip
Para evitar actualizar Pip en un sistema cuando se ejecuta localmente, verifique que estemos ejecutando en Colab. Por supuesto, los sistemas locales se pueden actualizar por separado.
try:
import colab
!pip install --upgrade pip
except:
pass
Instalar TFX
pip install -U tfx
¿Reinició el tiempo de ejecución?
Si está utilizando Google Colab, la primera vez que ejecuta la celda anterior, debe reiniciar el tiempo de ejecución haciendo clic sobre el botón "RESTART RUNTIME" o usando el menú "Runtime> Restart runtime ...". Esto se debe a la forma en que Colab carga los paquetes.
Consulta las versiones de TensorFlow y 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
Configurar variables
Hay algunas variables que se utilizan para definir una canalización. Puede personalizar estas variables como desee. De forma predeterminada, toda la salida de la canalización se generará en el directorio actual.
import os
# We will create two pipelines. One for schema generation and one for training.
SCHEMA_PIPELINE_NAME = "penguin-tfdv-schema"
PIPELINE_NAME = "penguin-tfdv"
# Output directory to store artifacts generated from the pipeline.
SCHEMA_PIPELINE_ROOT = os.path.join('pipelines', SCHEMA_PIPELINE_NAME)
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
SCHEMA_METADATA_PATH = os.path.join('metadata', SCHEMA_PIPELINE_NAME,
'metadata.db')
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.
Preparar datos de ejemplo
Descargaremos el conjunto de datos de ejemplo para usarlo en nuestra canalización TFX. El conjunto de datos que estamos utilizando es el conjunto de datos Palmer Pingüinos que también se utiliza en otros ejemplos TFX .
Hay cuatro características numéricas en este conjunto de datos:
- culmen_length_mm
- culmen_depth_mm
- flipper_length_mm
- body_mass_g
Todas las características ya se normalizaron para tener un rango [0,1]. Vamos a construir un modelo de clasificación que predice las species de pingüinos.
Debido a que el componente TFX ExampleGen lee las entradas de un directorio, necesitamos crear un directorio y copiar el conjunto de datos en él.
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-datan3p7t1d2/data.csv', <http.client.HTTPMessage at 0x7f8d2f9f9110>)
Eche un vistazo rápido al archivo 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
Debería poder ver cinco columnas de funciones. species es uno de 0, 1 ó 2, y todas las demás características deben tener valores entre 0 y 1. Vamos a crear una tubería TFX para analizar este conjunto de datos.
Genera un esquema preliminar
Las canalizaciones TFX se definen mediante las API de Python. Crearemos una canalización para generar un esquema a partir de los ejemplos de entrada automáticamente. Este esquema puede ser revisado por un humano y ajustado según sea necesario. Una vez que se finaliza el esquema, se puede usar para entrenamiento y validación de ejemplo en tareas posteriores.
Además de CsvExampleGen que se utiliza en simple TFX Pipeline tutorial , vamos a utilizar StatisticsGen y SchemaGen :
- StatisticsGen calcula estadísticas para el conjunto de datos.
- SchemaGen examina las estadísticas y crea un esquema de datos inicial.
Consulte las guías para cada componente o componentes TFX tutorial para aprender más sobre estos componentes.
Escribe una definición de canalización
Definimos una función para crear una tubería TFX. A Pipeline objeto representa una tubería TFX que se puede ejecutar usando uno de los sistemas de tubería orquestación que soportes TFX.
def _create_schema_pipeline(pipeline_name: str,
pipeline_root: str,
data_root: str,
metadata_path: str) -> tfx.dsl.Pipeline:
"""Creates a pipeline for schema generation."""
# Brings data into the pipeline.
example_gen = tfx.components.CsvExampleGen(input_base=data_root)
# NEW: Computes statistics over data for visualization and schema generation.
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
# NEW: Generates schema based on the generated statistics.
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)
components = [
example_gen,
statistics_gen,
schema_gen,
]
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)
Ejecutar la tubería
Vamos a utilizar LocalDagRunner como en el tutorial anterior.
tfx.orchestration.LocalDagRunner().run(
_create_schema_pipeline(
pipeline_name=SCHEMA_PIPELINE_NAME,
pipeline_root=SCHEMA_PIPELINE_ROOT,
data_root=DATA_ROOT,
metadata_path=SCHEMA_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: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: "SchemaGen"
value {
python_class_executable_spec {
class_path: "tfx.components.schema_gen.executor.Executor"
}
}
}
executor_specs {
key: "StatisticsGen"
value {
beam_executable_spec {
python_executor_spec {
class_path: "tfx.components.statistics_gen.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-tfdv-schema/metadata.db"
connection_mode: READWRITE_OPENCREATE
}
}
INFO:absl:Using connection config:
sqlite {
filename_uri: "metadata/penguin-tfdv-schema/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-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.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-datan3p7t1d2"
}
}
}
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"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 11:10:06.444468 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:06.453292 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:06.460209 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:06.467104 4006 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 11:10:06.521926 4006 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-tfdv-schema/CsvExampleGen/examples/1"
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329: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={'input_config': '{\n "splits": [\n {\n "name": "single_split",\n "pattern": "*"\n }\n ]\n}', '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-datan3p7t1d2', '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:1638702606,sum_checksum:1638702606'}, execution_output_uri='pipelines/penguin-tfdv-schema/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/CsvExampleGen/.system/stateful_working_dir/2021-12-05T11:10:06.420329', tmp_dir='pipelines/penguin-tfdv-schema/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-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.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-datan3p7t1d2"
}
}
}
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"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv-schema"
, pipeline_run_id='2021-12-05T11:10:06.420329')
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-datan3p7t1d2/* 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-tfdv-schema/CsvExampleGen/examples/1"
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329: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 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-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.StatisticsGen"
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}
inputs {
inputs {
key: "examples"
value {
channels {
producer_node_query {
id: "CsvExampleGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
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context_queries {
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name {
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}
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}
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: "SchemaGen"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:08.104562 4006 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 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-tfdv-schema/CsvExampleGen/examples/1"
properties {
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custom_properties {
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custom_properties {
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custom_properties {
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value {
string_value: "FORMAT_TF_EXAMPLE"
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}
custom_properties {
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value {
int_value: 0
}
}
custom_properties {
key: "tfx_version"
value {
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}
state: LIVE
create_time_since_epoch: 1638702608076
last_update_time_since_epoch: 1638702608076
, artifact_type: id: 15
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custom_properties {
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value {
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}
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
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)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv-schema/StatisticsGen/.system/executor_execution/2/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/StatisticsGen/.system/stateful_working_dir/2021-12-05T11:10:06.420329', tmp_dir='pipelines/penguin-tfdv-schema/StatisticsGen/.system/executor_execution/2/.temp/', pipeline_node=node_info {
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contexts {
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inputs {
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name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.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: "SchemaGen"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv-schema"
, pipeline_run_id='2021-12-05T11:10:06.420329')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2/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 2 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:StatisticsGen:statistics:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]}) for execution 2
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.schema_gen.component.SchemaGen"
}
id: "SchemaGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.SchemaGen"
}
}
}
}
inputs {
inputs {
key: "statistics"
value {
channels {
producer_node_query {
id: "StatisticsGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.StatisticsGen"
}
}
}
artifact_query {
type {
name: "ExampleStatistics"
}
}
output_key: "statistics"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "schema"
value {
artifact_spec {
type {
name: "Schema"
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
parameters {
key: "infer_feature_shape"
value {
field_value {
int_value: 1
}
}
}
}
upstream_nodes: "StatisticsGen"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:10.975282 4006 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={'statistics': [Artifact(artifact: id: 2
type_id: 17
uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:StatisticsGen:statistics:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
create_time_since_epoch: 1638702610957
last_update_time_since_epoch: 1638702610957
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/SchemaGen/schema/3"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:SchemaGen:schema:0"
}
}
, artifact_type: name: "Schema"
)]}), exec_properties={'exclude_splits': '[]', 'infer_feature_shape': 1}, execution_output_uri='pipelines/penguin-tfdv-schema/SchemaGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/SchemaGen/.system/stateful_working_dir/2021-12-05T11:10:06.420329', tmp_dir='pipelines/penguin-tfdv-schema/SchemaGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.schema_gen.component.SchemaGen"
}
id: "SchemaGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.SchemaGen"
}
}
}
}
inputs {
inputs {
key: "statistics"
value {
channels {
producer_node_query {
id: "StatisticsGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:06.420329"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.StatisticsGen"
}
}
}
artifact_query {
type {
name: "ExampleStatistics"
}
}
output_key: "statistics"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "schema"
value {
artifact_spec {
type {
name: "Schema"
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
parameters {
key: "infer_feature_shape"
value {
field_value {
int_value: 1
}
}
}
}
upstream_nodes: "StatisticsGen"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv-schema"
, pipeline_run_id='2021-12-05T11:10:06.420329')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to pipelines/penguin-tfdv-schema/SchemaGen/schema/3/schema.pbtxt.
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'>, {'schema': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/SchemaGen/schema/3"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:SchemaGen:schema:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "Schema"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.
I1205 11:10:11.010145 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
Debería ver "INFO: absl: Component SchemaGen está terminado". si la canalización terminó con éxito.
Examinaremos la salida de la canalización para comprender nuestro conjunto de datos.
Revisar los resultados de la tubería
Como se explica en el tutorial anterior, una tubería TFX produce dos tipos de salidas, objetos y una metadatos DB (MLMD) que contiene los metadatos de los artefactos y las ejecuciones de tuberías. Definimos la ubicación de estas salidas en las celdas anteriores. Por defecto, los artefactos son almacenados bajo la pipelines metadatos de directorio y se almacena como una base de datos SQLite bajo el metadata directorio.
Puede utilizar las API de MLMD para localizar estas salidas de forma programática. Primero, definiremos algunas funciones de utilidad para buscar los artefactos de salida que se acaban de producir.
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])
# Non-public APIs, just for showcase.
from tfx.orchestration.experimental.interactive import visualizations
def visualize_artifacts(artifacts):
"""Visualizes artifacts using standard visualization modules."""
for artifact in artifacts:
visualization = visualizations.get_registry().get_visualization(
artifact.type_name)
if visualization:
visualization.display(artifact)
from tfx.orchestration.experimental.interactive import standard_visualizations
standard_visualizations.register_standard_visualizations()
Ahora podemos examinar los resultados de la ejecución de la canalización.
# 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(
SCHEMA_METADATA_PATH)
with Metadata(metadata_connection_config) as metadata_handler:
# Find output artifacts from MLMD.
stat_gen_output = get_latest_artifacts(metadata_handler, SCHEMA_PIPELINE_NAME,
'StatisticsGen')
stats_artifacts = stat_gen_output[standard_component_specs.STATISTICS_KEY]
schema_gen_output = get_latest_artifacts(metadata_handler,
SCHEMA_PIPELINE_NAME, 'SchemaGen')
schema_artifacts = schema_gen_output[standard_component_specs.SCHEMA_KEY]
INFO:absl:MetadataStore with DB connection initialized
Es hora de examinar los resultados de cada componente. Como se describió anteriormente, Tensorflow validación de datos (TFDV) se utiliza en StatisticsGen y SchemaGen , y TFDV también proporciona la visualización de las salidas de estos componentes.
En este tutorial, usaremos los métodos de ayuda de visualización en TFX que usan TFDV internamente para mostrar la visualización.
Examine la salida de StatisticsGen
# docs-infra: no-execute
visualize_artifacts(stats_artifacts)
Puede ver varias estadísticas para los datos de entrada. Estas estadísticas se suministran a SchemaGen para construir un esquema inicial de datos automáticamente.
Examine la salida de SchemaGen
visualize_artifacts(schema_artifacts)
Este esquema se infiere automáticamente de la salida de StatisticsGen. Debería poder ver 4 funciones FLOAT y 1 función INT.
Exportar el esquema para uso futuro
Necesitamos revisar y refinar el esquema generado. El esquema revisado debe persistir para que se pueda usar en canalizaciones posteriores para el entrenamiento del modelo ML. En otras palabras, es posible que desee agregar el archivo de esquema a su sistema de control de versiones para casos de uso reales. En este tutorial, simplemente copiaremos el esquema en una ruta de sistema de archivos predefinida para simplificar.
import shutil
_schema_filename = 'schema.pbtxt'
SCHEMA_PATH = 'schema'
os.makedirs(SCHEMA_PATH, exist_ok=True)
_generated_path = os.path.join(schema_artifacts[0].uri, _schema_filename)
# Copy the 'schema.pbtxt' file from the artifact uri to a predefined path.
shutil.copy(_generated_path, SCHEMA_PATH)
'schema/schema.pbtxt'
El archivo de esquema utiliza formato de texto de búfer de protocolo y una instancia de TensorFlow metadatos Esquema proto .
print(f'Schema at {SCHEMA_PATH}-----')
!cat {SCHEMA_PATH}/*
Schema at schema-----
feature {
name: "body_mass_g"
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
shape {
dim {
size: 1
}
}
}
feature {
name: "culmen_depth_mm"
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
shape {
dim {
size: 1
}
}
}
feature {
name: "culmen_length_mm"
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
shape {
dim {
size: 1
}
}
}
feature {
name: "flipper_length_mm"
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
shape {
dim {
size: 1
}
}
}
feature {
name: "species"
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
shape {
dim {
size: 1
}
}
}
Debe asegurarse de revisar y posiblemente editar la definición del esquema según sea necesario. En este tutorial, solo usaremos el esquema generado sin cambios.
Validar ejemplos de entrada y entrenar un modelo de AA
Vamos a volver a la tubería que hemos creado en el simple TFX Pipeline Tutorial , para entrenar un modelo ML y utilizar el esquema generado por escribir el código de modelo de formación.
También vamos a añadir una ExampleValidator componente que va a buscar anomalías y valores que faltan en el conjunto de datos de entrada con respecto al esquema.
Escribe el código de entrenamiento del modelo
Tenemos que escribir el código del modelo como lo hicimos en simple TFX Pipeline Tutorial .
El modelo en sí es el mismo que en el tutorial anterior, pero esta vez usaremos el esquema generado a partir de la canalización anterior en lugar de especificar características manualmente. La mayor parte del código no se modificó. La única diferencia es que no necesitamos especificar los nombres y tipos de características en este archivo. En cambio, las leemos desde el archivo de esquema.
_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
# We don't need to specify _FEATURE_KEYS and _FEATURE_SPEC any more.
# Those information can be read from the given schema file.
_LABEL_KEY = 'species'
_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10
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(schema: schema_pb2.Schema) -> 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.
# ++ Changed code: Uses all features in the schema except the label.
feature_keys = [f.name for f in schema.feature if f.name != _LABEL_KEY]
inputs = [keras.layers.Input(shape=(1,), name=f) for f in feature_keys]
# ++ End of the changed code.
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.
"""
# ++ Changed code: Reads in schema file passed to the Trainer component.
schema = tfx.utils.parse_pbtxt_file(fn_args.schema_path, schema_pb2.Schema())
# ++ End of the changed code.
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(schema)
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
Ahora ha completado todos los pasos de preparación para crear una canalización TFX para el entrenamiento de modelos.
Escribe una definición de canalización
Vamos a añadir dos nuevos componentes, Importer y ExampleValidator . El importador trae un archivo externo a la canalización TFX. En este caso, es un archivo que contiene la definición del esquema. ExampleValidator examinará los datos de entrada y validará si todos los datos de entrada se ajustan al esquema de datos que proporcionamos.
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:
"""Creates a pipeline using predefined schema with TFX."""
# Brings data into the pipeline.
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'])
# NEW: Import the schema.
schema_importer = tfx.dsl.Importer(
source_uri=schema_path,
artifact_type=tfx.types.standard_artifacts.Schema).with_id(
'schema_importer')
# NEW: Performs anomaly detection based on statistics and data schema.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_importer.outputs['result'])
# Uses user-provided Python function that trains a model.
trainer = tfx.components.Trainer(
module_file=module_file,
examples=example_gen.outputs['examples'],
schema=schema_importer.outputs['result'], # Pass the imported schema.
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,
# NEW: Following three components were added to the pipeline.
statistics_gen,
schema_importer,
example_validator,
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)
Ejecutar la tubería
tfx.orchestration.LocalDagRunner().run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
schema_path=SCHEMA_PATH,
module_file=_trainer_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_trainer.py' (including modules: ['penguin_trainer']).
INFO:absl:User module package has hash fingerprint version 000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp50dqc5bp/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp6_kn7s87', '--dist-dir', '/tmp/tmpwt7plki0']
/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-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-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"
}
}
}
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-tfdv/metadata.db"
connection_mode: READWRITE_OPENCREATE
}
}
INFO:absl:Using connection config:
sqlite {
filename_uri: "metadata/penguin-tfdv/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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.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-datan3p7t1d2"
}
}
}
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"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:11.685647 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:11.692644 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:11.699625 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:11.708110 4006 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 11:10:11.722760 4006 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-tfdv/CsvExampleGen/examples/1"
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239: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={'input_base': '/tmp/tfx-datan3p7t1d2', 'input_config': '{\n "splits": [\n {\n "name": "single_split",\n "pattern": "*"\n }\n ]\n}', 'output_data_format': 6, '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}', 'output_file_format': 5, 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606'}, execution_output_uri='pipelines/penguin-tfdv/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/CsvExampleGen/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.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-datan3p7t1d2"
}
}
}
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"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /tmp/tfx-datan3p7t1d2/* to TFExample.
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_trainer.py -> build/lib
installing to /tmp/tmp6_kn7s87
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmp6_kn7s87
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/tmp6_kn7s87/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3.7.egg-info
running install_scripts
creating /tmp/tmp6_kn7s87/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/WHEEL
creating '/tmp/tmpwt7plki0/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl' and adding '/tmp/tmp6_kn7s87' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/RECORD'
removing /tmp/tmp6_kn7s87
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
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-tfdv/CsvExampleGen/examples/1"
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239: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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.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: "Trainer"
execution_options {
caching_options {
}
}
INFO:absl:Running as an importer node.
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:12.796727 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Processing source uri: schema, properties: {}, custom_properties: {}
INFO:absl:Component schema_importer is finished.
I1205 11:10:12.806819 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.StatisticsGen"
}
}
}
}
inputs {
inputs {
key: "examples"
value {
channels {
producer_node_query {
id: "CsvExampleGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.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 11:10:12.827589 4006 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-tfdv/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:1638702606,sum_checksum:1638702606"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239: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: 1638702612780
last_update_time_since_epoch: 1638702612780
, 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-tfdv/StatisticsGen/statistics/3"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:StatisticsGen:statistics:0"
}
}
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv/StatisticsGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/StatisticsGen/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/StatisticsGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.statistics_gen.component.StatisticsGen"
}
id: "StatisticsGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
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}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.StatisticsGen"
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}
inputs {
inputs {
key: "examples"
value {
channels {
producer_node_query {
id: "CsvExampleGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
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}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.CsvExampleGen"
}
}
}
artifact_query {
type {
name: "Examples"
}
}
output_key: "examples"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "statistics"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to pipelines/penguin-tfdv/StatisticsGen/statistics/3/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to pipelines/penguin-tfdv/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-tfdv/StatisticsGen/statistics/3"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:StatisticsGen:statistics:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component 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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.Trainer"
}
}
}
}
inputs {
inputs {
key: "examples"
value {
channels {
producer_node_query {
id: "CsvExampleGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
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}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
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context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.CsvExampleGen"
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}
}
artifact_query {
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name: "Examples"
}
}
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min_count: 1
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}
inputs {
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value {
channels {
producer_node_query {
id: "schema_importer"
}
context_queries {
type {
name: "pipeline"
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name {
field_value {
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context_queries {
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context_queries {
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}
artifact_query {
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}
output_key: "result"
}
}
}
}
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"
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}
}
parameters {
key: "eval_args"
value {
field_value {
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parameters {
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parameters {
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upstream_nodes: "CsvExampleGen"
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execution_options {
caching_options {
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}
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:15.426606 4006 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
type_id: 15
uri: "pipelines/penguin-tfdv/CsvExampleGen/examples/1"
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custom_properties {
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custom_properties {
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custom_properties {
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custom_properties {
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}
custom_properties {
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state: LIVE
create_time_since_epoch: 1638702612780
last_update_time_since_epoch: 1638702612780
, artifact_type: id: 15
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properties {
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properties {
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)], 'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
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state: LIVE
create_time_since_epoch: 1638702612810
last_update_time_since_epoch: 1638702612810
, artifact_type: id: 17
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custom_properties {
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id: "Trainer"
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contexts {
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contexts {
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inputs {
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context_queries {
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}
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upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
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execution_options {
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, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
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 {'eval_args': '{\n "num_steps": 5\n}', 'module_path': 'penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl', 'custom_config': 'null', 'train_args': '{\n "num_steps": 100\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbb1l9_v7', 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl']
Processing ./pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-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+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2
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:body_mass_g (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_length_mm (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate) (None, 4) 0 body_mass_g[0][0]
INFO:absl: culmen_depth_mm[0][0]
INFO:absl: culmen_length_mm[0][0]
INFO:absl: flipper_length_mm[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: 0.5752 - sparse_categorical_accuracy: 0.8165 - val_loss: 0.2294 - val_sparse_categorical_accuracy: 0.9400
2021-12-05 11:10:20.208161: 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-tfdv/Trainer/model/4/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-tfdv/Trainer/model/4/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-tfdv/Trainer/model/4/Format-Serving. ModelRun written to pipelines/penguin-tfdv/Trainer/model_run/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'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model_run/4"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model_run:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model:0"
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}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
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}
, artifact_type: name: "Model"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Trainer is finished.
I1205 11:10:20.766410 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:20.770478 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component ExampleValidator is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.example_validator.component.ExampleValidator"
}
id: "ExampleValidator"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
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}
}
contexts {
type {
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}
name {
field_value {
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contexts {
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name {
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value {
channels {
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context_queries {
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name {
field_value {
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context_queries {
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name {
field_value {
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context_queries {
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}
name {
field_value {
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inputs {
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value {
channels {
producer_node_query {
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context_queries {
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context_queries {
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artifact_query {
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output_key: "statistics"
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min_count: 1
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}
outputs {
outputs {
key: "anomalies"
value {
artifact_spec {
type {
name: "ExampleAnomalies"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
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parameters {
parameters {
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value {
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upstream_nodes: "StatisticsGen"
upstream_nodes: "schema_importer"
execution_options {
caching_options {
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}
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:20.793696 4006 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={'statistics': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
properties {
key: "split_names"
value {
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}
}
custom_properties {
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value {
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custom_properties {
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value {
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}
}
state: LIVE
create_time_since_epoch: 1638702615406
last_update_time_since_epoch: 1638702615406
, artifact_type: id: 19
name: "ExampleStatistics"
properties {
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properties {
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)], 'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
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value {
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}
state: LIVE
create_time_since_epoch: 1638702612810
last_update_time_since_epoch: 1638702612810
, artifact_type: id: 17
name: "Schema"
)]}, output_dict=defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-tfdv/ExampleValidator/anomalies/5"
custom_properties {
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, artifact_type: name: "ExampleAnomalies"
properties {
key: "span"
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properties {
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)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv/ExampleValidator/.system/executor_execution/5/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/ExampleValidator/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/ExampleValidator/.system/executor_execution/5/.temp/', pipeline_node=node_info {
type {
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id: "ExampleValidator"
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contexts {
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contexts {
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name {
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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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artifact_query {
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inputs {
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channels {
producer_node_query {
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context_queries {
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context_queries {
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name {
field_value {
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context_queries {
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outputs {
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parameters {
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upstream_nodes: "StatisticsGen"
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execution_options {
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, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to pipelines/penguin-tfdv/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-tfdv/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-tfdv/ExampleValidator/anomalies/5"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:ExampleValidator:anomalies:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "ExampleAnomalies"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component ExampleValidator is finished.
INFO:absl:Component Pusher is running.
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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.Pusher"
}
}
}
}
inputs {
inputs {
key: "model"
value {
channels {
producer_node_query {
id: "Trainer"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.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-tfdv\"\n }\n}"
}
}
}
}
upstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:20.848567 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 6
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'model': [Artifact(artifact: id: 5
type_id: 22
uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
create_time_since_epoch: 1638702620774
last_update_time_since_epoch: 1638702620774
, artifact_type: id: 22
name: "Model"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Pusher/pushed_model/6"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Pusher:pushed_model:0"
}
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'push_destination': '{\n "filesystem": {\n "base_directory": "serving_model/penguin-tfdv"\n }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-tfdv/Pusher/.system/executor_execution/6/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/Pusher/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/Pusher/.system/executor_execution/6/.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-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.Pusher"
}
}
}
}
inputs {
inputs {
key: "model"
value {
channels {
producer_node_query {
id: "Trainer"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T11:10:11.667239"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.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-tfdv\"\n }\n}"
}
}
}
}
upstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1638702620
INFO:absl:Model written to serving path serving_model/penguin-tfdv/1638702620.
INFO:absl:Model pushed to pipelines/penguin-tfdv/Pusher/pushed_model/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'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Pusher/pushed_model/6"
custom_properties {
key: "name"
value {
string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Pusher:pushed_model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "PushedModel"
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:20.879335 4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is finished.
Debería ver "INFO: absl: Component Pusher está terminado". si la canalización terminó con éxito.
Examinar los resultados de la tubería
Hemos entrenado el modelo de clasificación para pingüinos y también hemos validado los ejemplos de entrada en el componente ExampleValidator. Podemos analizar la salida de ExampleValidator como hicimos con la canalización anterior.
metadata_connection_config = tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH)
with Metadata(metadata_connection_config) as metadata_handler:
ev_output = get_latest_artifacts(metadata_handler, PIPELINE_NAME,
'ExampleValidator')
anomalies_artifacts = ev_output[standard_component_specs.ANOMALIES_KEY]
INFO:absl:MetadataStore with DB connection initialized
ExampleAnomalies del ExampleValidator también se pueden visualizar.
visualize_artifacts(anomalies_artifacts)
Debería ver "No se encontraron anomalías" para cada división de ejemplos. Debido a que usamos los mismos datos que se usaron para la generación del esquema en esta canalización, no se espera ninguna anomalía aquí. Si ejecuta esta canalización repetidamente con nuevos datos entrantes, ExampleValidator debería poder encontrar cualquier discrepancia entre los nuevos datos y el esquema existente.
Si se encontró alguna anomalía, puede revisar sus datos para verificar si algún ejemplo no sigue sus suposiciones. Los resultados de otros componentes como StatisticsGen pueden resultar útiles. Sin embargo, cualquier anomalía que se encuentre NO bloqueará más ejecuciones de canalizaciones.
Próximos pasos
Puede encontrar más recursos en https://www.tensorflow.org/tfx/tutorials
Por favor, vea Comprender TFX Tuberías para aprender más acerca de los diversos conceptos en TFX.