def testModelValidatorOnDataflowRunner(self):
"""Test for ModelValidatorEvaluator on DataflowRunner invocation."""
pipeline_name = 'kubeflow-model-validator-dataflow-test-{}'.format(
self._random_id())
pipeline = self._create_dataflow_pipeline(pipeline_name, [
ModelValidator(
examples=self._test_raw_examples, model=self._test_model),
])
self._compile_and_run_pipeline(pipeline)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
metadata_path: Text) -> pipeline.Pipeline:
"""Implements the Iris flowers pipeline with TFX."""
examples = external_input(data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(module_file=module_file,
examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['model'])
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats, trainer,
model_analyzer, model_validator, pusher
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
additional_pipeline_args={},
)
def create_pipelines():
examples = tfrecord_input(tfrecord_dir)
example_gen = ImportExampleGen(input_base=examples)
print('example-gen', example_gen.outputs.examples)
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
print('statistics-gen', statistics_gen.outputs.output)
infer_schema = SchemaGen(stats=statistics_gen.outputs.output,
infer_feature_shape=True)
print('schema-gen', infer_schema.outputs.output)
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
print('example-validator', validate_stats.outputs.output)
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=module_file)
print('transform', transform.outputs.transformed_examples)
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=100),
eval_args=trainer_pb2.EvalArgs(num_steps=50))
print('trainer', trainer.outputs.output)
model_analyzer = Evaluator(examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output)
print('model_analyzer', model_analyzer)
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
print('model_validator', model_validator)
pusher = Pusher(model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
print('pusher', pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=airflow_pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
enable_cache=True,
metadata_db_root=metadata_db_root,
additional_pipeline_args={'logger_args': logger_overrides})
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
examples = csv_input(_data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=_taxi_module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=_taxi_module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
]
def create_e2e_components(
pipeline_root: Text,
csv_input_location: Text,
transform_module: Text,
trainer_module: Text,
) -> List[BaseComponent]:
"""Creates components for a simple Chicago Taxi TFX pipeline for testing.
Args:
pipeline_root: The root of the pipeline output.
csv_input_location: The location of the input data directory.
transform_module: The location of the transform module file.
trainer_module: The location of the trainer module file.
Returns:
A list of TFX components that constitutes an end-to-end test pipeline.
"""
examples = dsl_utils.external_input(csv_input_location)
example_gen = CsvExampleGen(input=examples)
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
transform = Transform(input_data=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=transform_module)
trainer = Trainer(
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10),
eval_args=trainer_pb2.EvalArgs(num_steps=5),
module_file=trainer_module)
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=os.path.join(pipeline_root, 'model_serving'))))
return [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
]
def testModelValidatorOnDataflowRunner(self):
"""ModelValidator-only test pipeline on DataflowRunner."""
pipeline_name = 'kubeflow-evaluator-dataflow-test-{}'.format(
self._random_id())
pipeline = self._create_dataflow_pipeline(pipeline_name, [
ModelValidator(examples=self._input_artifacts(
pipeline_name, self._test_raw_examples),
model=self._input_artifacts(pipeline_name,
self._test_model))
])
self._compile_and_run_pipeline(pipeline)
def create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
examples = csv_input(os.path.join(data_root, 'simple'))
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(
input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=taxi_module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=taxi_module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_steps=10000,
eval_steps=5000,
warm_starting=True)
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output)
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(
examples=example_gen.outputs.examples, model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
serving_model_dir=serving_model_dir)
return [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
]
def testModelValidatorOnDataflowRunner(self):
"""ModelValidator-only test pipeline on DataflowRunner."""
pipeline_name = 'kubeflow-evaluator-dataflow-test-{}'.format(
self._random_id())
pipeline = self._create_dataflow_pipeline(pipeline_name, [
self.raw_examples_importer, self.model_1_importer,
ModelValidator(
examples=self.raw_examples_importer.outputs['result'],
model=self.model_1_importer.outputs['result'])
])
self._compile_and_run_pipeline(pipeline)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text) -> pipeline.Pipeline:
examples = external_input(data_root)
input_split = example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='train', pattern='train.tfrecord'),
example_gen_pb2.Input.Split(name='eval', pattern='test.tfrecord')
])
example_gen = ImportExampleGen(input_base=examples, input_config=input_split)
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
validate_stats = ExampleValidator(
stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
transform = Transform(
input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=module_file)
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=1000),
eval_args=trainer_pb2.EvalArgs(num_steps=500))
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec()
]))
model_validator = ModelValidator(
examples=example_gen.outputs.examples, model=trainer.outputs.output)
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
],
additional_pipeline_args={
'tfx_image': 'tensorflow/tfx:0.14.0rc1'
},
log_root='/var/tmp/tfx/logs',
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
direct_num_workers: int) -> pipeline.Pipeline:
examples = external_input(data_root)
input_split = example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='train', pattern='train.tfrecord'),
example_gen_pb2.Input.Split(name='eval', pattern='test.tfrecord')
])
example_gen = ImportExampleGen(input=examples, input_config=input_split)
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
transform = Transform(examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
eval_config = tfma.EvalConfig(slicing_specs=[tfma.SlicingSpec()])
model_analyzer = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
eval_config=eval_config)
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers])
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
metadata_path: Text) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
examples = external_input(data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
additional_pipeline_args={
# LINT.IfChange
'beam_pipeline_args': [
# ----- Beam Args -----.
'--runner=PortableRunner',
# Points to the job server started in
# setup_beam_on_(flink|spark).sh
'--job_endpoint=localhost:8099',
'--environment_type=LOOPBACK',
# TODO(BEAM-6754): Utilize multicore in LOOPBACK environment. # pylint: disable=g-bad-todo
# TODO(BEAM-5167): Use concurrency information from SDK Harness. # pylint: disable=g-bad-todo
# Note; We use 100 worker threads to mitigate the issue with
# scheduling work between the Beam runner and SDK harness. Flink
# and Spark can process unlimited work items concurrently while
# SdkHarness can only process 1 work item per worker thread.
# Having 100 threads will let 100 tasks execute concurrently
# avoiding scheduling issue in most cases. In case the threads are
# exhausted, beam print the relevant message in the log.
'--experiments=worker_threads=100',
# TODO(BEAM-7199): Obviate the need for setting pre_optimize=all. # pylint: disable=g-bad-todo
'--experiments=pre_optimize=all',
# ----- Flink runner-specific Args -----.
# TODO(b/126725506): Set the task parallelism based on cpu cores.
# TODO(FLINK-10672): Obviate setting BATCH_FORCED.
'--execution_mode_for_batch=BATCH_FORCED',
],
# LINT.ThenChange(setup/setup_beam_on_spark.sh)
# LINT.ThenChange(../chicago_taxi/setup_beam_on_flink.sh)
})
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=_query)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=_taxi_utils)
# Uses user-provided Python function that implements a model using TF-Learn
# to train a model on Google Cloud AI Platform.
try:
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor # pylint: disable=g-import-not-at-top
# Train using a custom executor. This requires TFX >= 0.14.
trainer = Trainer(
executor_class=ai_platform_trainer_executor.Executor,
module_file=_taxi_utils,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={
'ai_platform_training_args': _ai_platform_training_args
})
except ImportError:
# Train using a deprecated flag.
trainer = Trainer(
module_file=_taxi_utils,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'cmle_training_args': _ai_platform_training_args})
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a destination if check passed.
try:
from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor # pylint: disable=g-import-not-at-top
# Deploy the model on Google Cloud AI Platform. This requires TFX >=0.14.
pusher = Pusher(executor_class=ai_platform_pusher_executor.Executor,
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={
'ai_platform_serving_args':
_ai_platform_serving_args
},
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
except ImportError:
# Deploy the model on Google Cloud AI Platform, using a deprecated flag.
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={'cmle_serving_args': _ai_platform_serving_args},
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return pipeline.Pipeline(
pipeline_name='chicago_taxi_pipeline_kubeflow',
pipeline_root=_pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
additional_pipeline_args={
'beam_pipeline_args': [
'--runner=DataflowRunner',
'--experiments=shuffle_mode=auto',
'--project=' + _project_id,
'--temp_location=' + os.path.join(_output_bucket, 'tmp'),
'--region=' + _gcp_region,
],
# Optional args:
# 'tfx_image': custom docker image to use for components.
# This is needed if TFX package is not installed from an RC
# or released version.
},
log_root='/var/tmp/tfx/logs',
)
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
query = """
SELECT
pickup_community_area,
fare,
EXTRACT(MONTH FROM trip_start_timestamp) AS trip_start_month,
EXTRACT(HOUR FROM trip_start_timestamp) AS trip_start_hour,
EXTRACT(DAYOFWEEK FROM trip_start_timestamp) AS trip_start_day,
UNIX_SECONDS(trip_start_timestamp) AS trip_start_timestamp,
pickup_latitude,
pickup_longitude,
dropoff_latitude,
dropoff_longitude,
trip_miles,
pickup_census_tract,
dropoff_census_tract,
payment_type,
company,
trip_seconds,
dropoff_community_area,
tips
FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`
WHERE (ABS(FARM_FINGERPRINT(unique_key)) / {max_int64})
< {query_sample_rate}""".format(
max_int64=_max_int64, query_sample_rate=_query_sample_rate)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=query)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
stats=statistics_gen.outputs.output, schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(
input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=_taxi_utils)
# Uses user-provided Python function that implements a model using TF-Learn
# to train a model on Google Cloud AI Platform.
try:
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor # pylint: disable=g-import-not-at-top
# Train using a custom executor. This requires TFX >= 0.14.
trainer = Trainer(
executor_class=ai_platform_trainer_executor.Executor,
module_file=_taxi_utils,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'ai_platform_training_args': _ai_platform_training_args})
except ImportError:
# Train using a deprecated flag.
trainer = Trainer(
module_file=_taxi_utils,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'cmle_training_args': _ai_platform_training_args})
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(
examples=example_gen.outputs.examples, model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a destination if check passed.
try:
from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor # pylint: disable=g-import-not-at-top
# Deploy the model on Google Cloud AI Platform. This requires TFX >=0.14.
pusher = Pusher(
executor_class=ai_platform_pusher_executor.Executor,
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={'ai_platform_serving_args': _ai_platform_serving_args},
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
except ImportError:
# Deploy the model on Google Cloud AI Platform, using a deprecated flag.
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={'cmle_serving_args': _ai_platform_serving_args},
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
]
def create_pipeline(pipeline_name, pipeline_root, input_path,
tf_transform_file, tf_trainer_file, serving_model_basedir,
**kwargs):
examples = tfrecord_input(input_path)
input_config = example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='tfrecord',
pattern='data_tfrecord-*.gz'),
]) # todo add as airflow var
output_config = example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(
name='train', hash_buckets=2), # todo add as airflow var
example_gen_pb2.SplitConfig.Split(
name='eval', hash_buckets=1) # todo add as airflow var
]))
example_gen = ImportExampleGen(input_base=examples,
input_config=input_config,
output_config=output_config)
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=tf_transform_file)
trainer = Trainer(
module_file=tf_trainer_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=[]) # todo add your slicing column
]))
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
pusher = Pusher(model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_basedir)))
pipeline = Pipeline(pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
**kwargs)
pipeline.components = [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
]
return pipeline
def _create_pipeline(
pipeline_name: Text, pipeline_root: Text, query: Text, module_file: Text,
beam_pipeline_args: List[Text], ai_platform_training_args: Dict[Text, Text],
bigquery_serving_args: Dict[Text, Text]) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=query)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(
statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(
examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn
# to train a model on Google Cloud AI Platform.
trainer = Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_trainer_executor.Executor),
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'ai_platform_training_args': ai_platform_training_args})
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(
examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
# Checks whether the model passed the validation steps and pushes the model
# to Google Cloud BigQuery ML if check passed.
pusher = Pusher(
custom_executor_spec=executor_spec.ExecutorClassSpec(
bigquery_ml_pusher_executor.Executor),
model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
custom_config={'bigquery_serving_args': bigquery_serving_args})
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
],
beam_pipeline_args=beam_pipeline_args,
)
def _create_test_pipeline(pipeline_root: Text, csv_input_location: Text,
taxi_module_file: Text, enable_cache: bool):
"""Creates a simple Kubeflow-based Chicago Taxi TFX pipeline.
Args:
pipeline_name: The name of the pipeline.
pipeline_root: The root of the pipeline output.
csv_input_location: The location of the input data directory.
taxi_module_file: The location of the module file for Transform/Trainer.
enable_cache: Whether to enable cache or not.
Returns:
A logical TFX pipeline.Pipeline object.
"""
examples = csv_input(csv_input_location)
example_gen = CsvExampleGen(input_base=examples)
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
infer_schema = SchemaGen(
stats=statistics_gen.outputs.output, infer_feature_shape=False)
validate_stats = ExampleValidator(
stats=statistics_gen.outputs.output, schema=infer_schema.outputs.output)
transform = Transform(
input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=taxi_module_file)
trainer = Trainer(
module_file=taxi_module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10),
eval_args=trainer_pb2.EvalArgs(num_steps=5))
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
model_validator = ModelValidator(
examples=example_gen.outputs.examples, model=trainer.outputs.output)
# Hack: ensuring push_destination can be correctly parameterized and interpreted.
# pipeline root will be specified as a dsl.PipelineParam with the name
# pipeline-root, see:
# https://github.com/tensorflow/tfx/blob/1c670e92143c7856f67a866f721b8a9368ede385/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L226
_pipeline_root_param = dsl.PipelineParam(name='pipeline-root')
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=os.path.join(str(_pipeline_root_param), 'model_serving'))))
return pipeline.Pipeline(
pipeline_name='parameterized_tfx_oss',
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
],
enable_cache=enable_cache,
)
def _create_pipeline(pipeline_name: Text,
pipeline_root: Text,
data_root: Text,
module_file: Text,
serving_model_dir: Text,
metadata_path: Text,
direct_num_workers: int = 1) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
examples = external_input(data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
# Note that direct_num_workers != 1 will enable multi-process for TFX,
# we hide the FnApiRunner[1] setting from user, but this is subject to
# change if Beam offers pure flag setup.
# [1]https://issues.apache.org/jira/browse/BEAM-3645
beam_pipeline_args=['--direct_num_workers=%s' % direct_num_workers],
additional_pipeline_args={},
)
def _create_parameterized_pipeline(
pipeline_name: Text,
pipeline_root: Optional[Text] = _pipeline_root,
enable_cache: Optional[bool] = True,
direct_num_workers: Optional[int] = 1) -> pipeline.Pipeline:
"""Creates a simple TFX pipeline with RuntimeParameter.
Args:
pipeline_name: The name of the pipeline.
pipeline_root: The root of the pipeline output.
enable_cache: Whether to enable cache in this pipeline.
direct_num_workers: Number of workers executing the underlying beam pipeline
in the executors.
Returns:
A logical TFX pipeline.Pipeline object.
"""
# First, define the pipeline parameters.
# Path to the CSV data file, under which there should be a data.csv file.
data_root = data_types.RuntimeParameter(
name='data-root',
default='gs://my-bucket/data',
ptype=Text,
)
# Path to the transform module file.
transform_module_file = data_types.RuntimeParameter(
name='transform-module',
default='gs://my-bucket/modules/transform_module.py',
ptype=Text,
)
# Path to the trainer module file.
trainer_module_file = data_types.RuntimeParameter(
name='trainer-module',
default='gs://my-bucket/modules/trainer_module.py',
ptype=Text,
)
# Number of epochs in training.
train_steps = data_types.RuntimeParameter(
name='train-steps',
default=10,
ptype=int,
)
# Number of epochs in evaluation.
eval_steps = data_types.RuntimeParameter(
name='eval-steps',
default=5,
ptype=int,
)
# Column name for slicing.
slicing_column = data_types.RuntimeParameter(
name='slicing-column',
default='trip_start_hour',
ptype=Text,
)
# The input data location is parameterized by data_root
examples = external_input(data_root)
example_gen = CsvExampleGen(input=examples)
statistics_gen = StatisticsGen(input_data=example_gen.outputs['examples'])
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# The module file used in Transform and Trainer component is paramterized by
# transform_module_file.
transform = Transform(input_data=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=transform_module_file)
# The numbers of steps in train_args are specified as RuntimeParameter with
# name 'train-steps' and 'eval-steps', respectively.
trainer = Trainer(
module_file=trainer_module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_output=transform.outputs['transform_graph'],
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps})
# The name of slicing column is specified as a RuntimeParameter.
model_analyzer = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
feature_slicing_spec=dict(specs=[{
'column_for_slicing': [slicing_column]
}]))
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
pusher = Pusher(
model_export=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=os.path.join(str(pipeline.ROOT_PARAMETER),
'model_serving'))))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
enable_cache=enable_cache,
# TODO(b/142684737): The multi-processing API might change.
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers],
)
def _create_pipeline(pipeline_root: Text,
csv_input_location: data_types.RuntimeParameter,
taxi_module_file: data_types.RuntimeParameter,
enable_cache: bool):
"""Creates a simple Kubeflow-based Chicago Taxi TFX pipeline.
Args:
pipeline_root: The root of the pipeline output.
csv_input_location: The location of the input data directory.
taxi_module_file: The location of the module file for Transform/Trainer.
enable_cache: Whether to enable cache or not.
Returns:
A logical TFX pipeline.Pipeline object.
"""
examples = external_input(csv_input_location)
example_gen = CsvExampleGen(input=examples)
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
infer_schema = SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False,
)
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'],
)
transform = Transform(
examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=taxi_module_file,
)
trainer = Trainer(
module_file=taxi_module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10),
eval_args=trainer_pb2.EvalArgs(num_steps=5),
)
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]),
)
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=os.path.join(str(pipeline.ROOT_PARAMETER),
'model_serving'))),
)
return pipeline.Pipeline(
pipeline_name='parameterized_tfx_oss',
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
enable_cache=enable_cache,
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text,
training_data_root: Text, inference_data_root: Text,
module_file: Text, metadata_path: Text,
direct_num_workers: int) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
training_examples = external_input(training_data_root)
# Brings training data into the pipeline or otherwise joins/converts
# training data.
training_example_gen = CsvExampleGen(input_base=training_examples,
instance_name='training_example_gen')
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(
input_data=training_example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs['output'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs['output'],
schema=infer_schema.outputs['output'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=training_example_gen.outputs['examples'],
schema=infer_schema.outputs['output'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['output'],
transform_output=transform.outputs['transform_output'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=training_example_gen.outputs['examples'],
model_exports=trainer.outputs['output'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(
examples=training_example_gen.outputs['examples'],
model=trainer.outputs['output'])
inference_examples = external_input(inference_data_root)
# Brings inference data into the pipeline.
inference_example_gen = CsvExampleGen(
input_base=inference_examples,
output_config=example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(name='unlabelled',
hash_buckets=100)
])),
instance_name='inference_example_gen')
# Performs offline batch inference over inference examples.
bulk_inferrer = BulkInferrer(
examples=inference_example_gen.outputs['examples'],
model_export=trainer.outputs['output'],
model_blessing=model_validator.outputs['blessing'],
# Empty data_spec.example_splits will result in using all splits.
data_spec=bulk_inferrer_pb2.DataSpec(),
model_spec=bulk_inferrer_pb2.ModelSpec())
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
training_example_gen, inference_example_gen, statistics_gen,
infer_schema, validate_stats, transform, trainer, model_analyzer,
model_validator, bulk_inferrer
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
# TODO(b/141578059): The multi-processing API might change.
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers])
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
examples = csv_input(_data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
stats=statistics_gen.outputs.output, schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(
input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=_taxi_module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=_taxi_module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(
examples=example_gen.outputs.examples, model=trainer.outputs.output)
# This custom component serves as a bridge between pipeline and human model
# reviewers to enable review-and-push workflow in model development cycle. It
# utilizes Slack API to send message to user-defined Slack channel with model
# URI info and wait for go / no-go decision from the same Slack channel:
# * To approve the model, users need to reply the thread sent out by the bot
# started by SlackComponent with 'lgtm' or 'approve'.
# * To reject the model, users need to reply the thread sent out by the bot
# started by SlackComponent with 'decline' or 'reject'.
slack_validator = SlackComponent(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
slack_token=_slack_token,
channel_id=_channel_id,
timeout_sec=3600,
)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=slack_validator.outputs.slack_blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, slack_validator, pusher
]
def _create__pipeline(pipeline_name: Text, pipeline_root: Text,
data_root: Text, module_file: Text,
ai_platform_training_args: Dict[Text, Text],
ai_platform_serving_args: Dict[Text, Text],
beam_pipeline_args: List[Text]) -> pipeline.Pipeline:
"""Implements the online news pipeline with TFX."""
examples = external_input(data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=module_file)
# Uses user-provided Python function that implements a model using
# TensorFlow's Estimators API.
trainer = Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_trainer_executor.Executor),
module_file=module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'ai_platform_training_args': ai_platform_training_args})
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(column_for_slicing=['weekday'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_pusher_executor.Executor),
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={'ai_platform_serving_args': ai_platform_serving_args})
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
# enable_cache=True,
beam_pipeline_args=beam_pipeline_args)
def _create_parameterized_pipeline(
pipeline_name: Text,
pipeline_root: Optional[Text] = _pipeline_root,
enable_cache: Optional[bool] = True,
direct_num_workers: Optional[int] = 1) -> pipeline.Pipeline:
"""Creates a simple TFX pipeline with RuntimeParameter.
Args:
pipeline_name: The name of the pipeline.
pipeline_root: The root of the pipeline output.
enable_cache: Whether to enable cache in this pipeline.
direct_num_workers: Number of workers executing the underlying beam pipeline
in the executors.
Returns:
A logical TFX pipeline.Pipeline object.
"""
# First, define the pipeline parameters.
# Path to the CSV data file, under which there should be a data.csv file.
data_root_param = data_types.RuntimeParameter(
name='data-root',
default='gs://my-bucket/data',
ptype=Text,
)
# Path to the module file.
taxi_module_file_param = data_types.RuntimeParameter(
name='module-file',
default='gs://my-bucket/modules/taxi_utils.py',
ptype=Text,
)
# Number of epochs in training.
train_steps = data_types.RuntimeParameter(
name='train-steps',
default=10,
ptype=int,
)
# Number of epochs in evaluation.
eval_steps = data_types.RuntimeParameter(
name='eval-steps',
default=5,
ptype=int,
)
# Column name for slicing.
slicing_column = data_types.RuntimeParameter(
name='slicing-column',
default='trip_start_hour',
ptype=Text,
)
# The input data location is parameterized by _data_root_param
examples = external_input(data_root_param)
example_gen = CsvExampleGen(input=examples)
statistics_gen = StatisticsGen(input_data=example_gen.outputs['examples'])
infer_schema = SchemaGen(
stats=statistics_gen.outputs['statistics'], infer_feature_shape=False)
validate_stats = ExampleValidator(
stats=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# The module file used in Transform and Trainer component is paramterized by
# _taxi_module_file_param.
transform = Transform(
input_data=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=taxi_module_file_param)
# The numbers of steps in train_args are specified as RuntimeParameter with
# name 'train-steps' and 'eval-steps', respectively.
trainer = Trainer(
module_file=taxi_module_file_param,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_output=transform.outputs['transform_graph'],
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps})
# The name of slicing column is specified as a RuntimeParameter.
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['model'],
feature_slicing_spec=dict(specs=[{
'column_for_slicing': [slicing_column]
}]))
model_validator = ModelValidator(
examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
# TODO(b/145949533) Currently we use this hack to ensure push_destination can
# be correctly parameterized and interpreted.
# pipeline root will be specified as a dsl.PipelineParam with the name
# pipeline-root, see:
# https://github.com/tensorflow/tfx/blob/1c670e92143c7856f67a866f721b8a9368ede385/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L226
pipeline_root_param = dsl.PipelineParam(name='pipeline-root')
pusher = Pusher(
model_export=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=os.path.join(
str(pipeline_root_param), 'model_serving'))))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
],
enable_cache=enable_cache,
# TODO(b/141578059): The multi-processing API might change.
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers],
)
def _create_test_pipeline(pipeline_name: Text, pipeline_root: Text,
csv_input_location: Text, taxi_module_file: Text,
container_image: Text):
"""Creates a simple Kubeflow-based Chicago Taxi TFX pipeline for testing.
Args:
pipeline_name: The name of the pipeline.
pipeline_root: The root of the pipeline output.
csv_input_location: The location of the input data directory.
taxi_module_file: The location of the module file for Transform/Trainer.
container_image: The container image to use.
Returns:
A logical TFX pipeline.Pipeline object.
"""
examples = dsl_utils.csv_input(csv_input_location)
example_gen = CsvExampleGen(input_base=examples)
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
validate_stats = ExampleValidator( # pylint: disable=unused-variable
stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=taxi_module_file)
trainer = Trainer(
module_file=taxi_module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
model_analyzer = Evaluator( # pylint: disable=unused-variable
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
pusher = Pusher( # pylint: disable=unused-variable
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=os.path.join(pipeline_root, 'model_serving'))))
return tfx_pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
log_root='/var/tmp/tfx/logs',
additional_pipeline_args={
'tfx_image': container_image,
},
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
direct_num_workers: int) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""
examples = external_input(data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn
# to train a model on Google Cloud AI Platform.
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
)
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
# Checks whether the model passed the validation steps and pushes the model
# to Google Cloud AI Platform if check passed.
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
# TODO(b/141578059): The multi-processing API might change.
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers],
additional_pipeline_args={},
)
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
examples = csv_input(_data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=examples)
# Computes statistics over data for visualization and example validation.
# pylint: disable=line-too-long
statistics_gen = StatisticsGen(
input_data=example_gen.outputs.examples) # Step 3
# pylint: enable=line-too-long
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output) # Step 3
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator( # Step 3
stats=statistics_gen.outputs.output, # Step 3
schema=infer_schema.outputs.output) # Step 3
# Performs transformations and feature engineering in training and serving.
transform = Transform( # Step 4
input_data=example_gen.outputs.examples, # Step 4
schema=infer_schema.outputs.output, # Step 4
module_file=_taxi_module_file) # Step 4
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer( # Step 5
module_file=_taxi_module_file, # Step 5
transformed_examples=transform.outputs.transformed_examples, # Step 5
schema=infer_schema.outputs.output, # Step 5
transform_output=transform.outputs.transform_output, # Step 5
train_args=trainer_pb2.TrainArgs(num_steps=10000), # Step 5
eval_args=trainer_pb2.EvalArgs(num_steps=5000)) # Step 5
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator( # Step 6
examples=example_gen.outputs.examples, # Step 6
model_exports=trainer.outputs.output, # Step 6
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[ # Step 6
evaluator_pb2.SingleSlicingSpec( # Step 6
column_for_slicing=['trip_start_hour']) # Step 6
])) # Step 6
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator( # Step 7
examples=example_gen.outputs.examples, # Step 7
model=trainer.outputs.output) # Step 7
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher( # Step 7
model_export=trainer.outputs.output, # Step 7
model_blessing=model_validator.outputs.blessing, # Step 7
push_destination=pusher_pb2.PushDestination( # Step 7
filesystem=pusher_pb2.PushDestination.Filesystem( # Step 7
base_directory=_serving_model_dir))) # Step 7
return pipeline.Pipeline(
pipeline_name='taxi',
pipeline_root=_pipeline_root,
components=[
example_gen,
statistics_gen,
infer_schema,
validate_stats, # Step 3
transform, # Step 4
trainer, # Step 5
# model_analyzer, # Step 6
# model_validator, pusher # Step 7
],
enable_cache=True,
metadata_db_root=_metadata_db_root,
additional_pipeline_args={'logger_args': logger_overrides},
)
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
query = """
SELECT
pickup_community_area,
fare,
EXTRACT(MONTH FROM trip_start_timestamp) AS trip_start_month,
EXTRACT(HOUR FROM trip_start_timestamp) AS trip_start_hour,
EXTRACT(DAYOFWEEK FROM trip_start_timestamp) AS trip_start_day,
UNIX_SECONDS(trip_start_timestamp) AS trip_start_timestamp,
pickup_latitude,
pickup_longitude,
dropoff_latitude,
dropoff_longitude,
trip_miles,
pickup_census_tract,
dropoff_census_tract,
payment_type,
company,
trip_seconds,
dropoff_community_area,
tips
FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`
WHERE RAND() < {}""".format(_query_sample_rate)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=query)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=_taxi_utils)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=_taxi_utils,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'cmle_training_args': _cmle_training_args})
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={'cmle_serving_args': _cmle_serving_args},
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return [
example_gen, statistics_gen, infer_schema, validate_stats, transform,
trainer, model_analyzer, model_validator, pusher
]
def _create_pipeline(
pipeline_name: Text, pipeline_root: Text, query: Text,
module_file: Text, serving_model_dir: Text,
beam_pipeline_args: List[Text], ai_platform_training_args: Dict[Text,
Text],
ai_platform_serving_args: Dict[Text, Text]) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=query)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output,
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn
# to train a model on Google Cloud AI Platform.
try:
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor # pylint: disable=g-import-not-at-top
# Train using a custom executor. This requires TFX >= 0.14.
trainer = Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_trainer_executor.Executor),
module_file=module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={
'ai_platform_training_args': ai_platform_training_args
})
except ImportError:
# Train using a deprecated flag.
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
transform_output=transform.outputs.transform_output,
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000),
custom_config={'cmle_training_args': ai_platform_training_args})
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output,
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
# Checks whether the model passed the validation steps and pushes the model
# to a destination if check passed.
try:
from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor # pylint: disable=g-import-not-at-top
# Deploy the model on Google Cloud AI Platform. This requires TFX >=0.14.
pusher = Pusher(custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_pusher_executor.Executor),
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={
'ai_platform_serving_args':
ai_platform_serving_args
})
except ImportError:
# Deploy the model on Google Cloud AI Platform, using a deprecated flag.
pusher = Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
custom_config={'cmle_serving_args': ai_platform_serving_args},
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
additional_pipeline_args={
'beam_pipeline_args': beam_pipeline_args,
},
log_root='/var/tmp/tfx/logs',
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text,
module_file: Text,
presto_config: presto_config_pb2.PrestoConnConfig,
query: Text, serving_model_dir: Text,
metadata_path: Text) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
# Brings data into the pipeline or otherwise joins/converts training data
example_gen = PrestoExampleGen(presto_config, query=query)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs['output'])
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(stats=statistics_gen.outputs['output'],
schema=infer_schema.outputs['output'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(input_data=example_gen.outputs['examples'],
schema=infer_schema.outputs['output'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
module_file=module_file,
transformed_examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['output'],
transform_output=transform.outputs['transform_output'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['output'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['output'])
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model_export=trainer.outputs['output'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, model_analyzer, model_validator, pusher
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
additional_pipeline_args={},
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
metadata_path: Text) -> pipeline.Pipeline:
"""Implements the cifar10 pipeline with TFX."""
examples = external_input(data_root)
input_split = example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='train', pattern='train.tfrecord'),
example_gen_pb2.Input.Split(name='eval', pattern='test.tfrecord')
])
example_gen = ImportExampleGen(input=examples, input_config=input_split)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(module_file=module_file,
examples=transform.outputs['transformed_examples'],
schema=infer_schema.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=1000),
eval_args=trainer_pb2.EvalArgs(num_steps=500))
# Uses TFMA to compute a evaluation statistics over features of a model.
evaluator = Evaluator(
examples=example_gen.outputs['examples'],
model_exports=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(
specs=[evaluator_pb2.SingleSlicingSpec()]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, infer_schema, validate_stats,
transform, trainer, evaluator, model_validator, pusher
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
additional_pipeline_args={},
)
