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=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'])
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
return pipeline.Pipeline(
pipeline_name='chicago_taxi_simple',
pipeline_root=_pipeline_root,
components=[example_gen, statistics_gen, infer_schema, validate_stats],
enable_cache=True,
metadata_db_root=_metadata_db_root,
)
def create_pipeline():
# Read data in; can split data here
examples = csv_input(DATA_DIR)
example_gen = CsvExampleGen(input_base=examples, name='iris_example')
# Generate feature statistics
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Infer schema for data
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
# Identify anomomalies in training and serving data
validate_stats = ExampleValidator(stats=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
# Performs feature engineering; emits a SavedModel that does preprocessing
transform = Transform(input_data=example_gen.outputs.examples,
schema=infer_schema.outputs.output,
module_file=TRANSFORM_MODULE_FILE)
# Trains a model
trainer = Trainer(
module_file=MODEL_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))
# Evaluates the model on different slices of the data (bias detection?!)
model_analyzer = Evaluator(examples=example_gen.outputs.examples,
model_exports=trainer.outputs.output)
# Compares new model against a baseline; both models evaluated on a dataset
model_validator = ModelValidator(examples=example_gen.outputs.examples,
model=trainer.outputs.output)
# Pushes a blessed model to a deployment target (tfserving)
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_DIR)))
return pipeline.Pipeline(pipeline_name=PIPELINE_NAME,
pipeline_root=DAGS_DIR,
components=[
example_gen, statistics_gen, infer_schema,
validate_stats, transform, trainer,
model_analyzer, model_validator, pusher
],
enable_cache=True,
metadata_db_root=METADATA_DIR,
additional_pipeline_args={
'logger_args': {
'log_root': LOGS_DIR,
'log_level': logging.INFO
}
})
def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""
examples = csv_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'])
return pipeline.Pipeline(
pipeline_name='chicago_taxi_pipeline_kubeflow',
pipeline_root=_pipeline_root,
components=[example_gen, statistics_gen, infer_schema],
additional_pipeline_args={
'beam_pipeline_args': [
'--runner=DataflowRunner',
'--experiments=shuffle_mode=auto',
'--project=' + _project_id,
'--temp_location=' + os.path.join(_output_dir, 'tmp'),
'--region=' + _gcp_region,
],
},
log_root='/var/tmp/tfx/logs',
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: 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_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'])
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[example_gen, statistics_gen, infer_schema, validate_stats],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
additional_pipeline_args={},
)
def _create_test_pipeline(pipeline_root: Text, csv_input_location: Text,
taxi_module_file: Text, output_bucket: 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=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=['trip_start_hour'])
]))
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=os.path.join(output_bucket, 'model_serving'))))
return pipeline.Pipeline(
pipeline_name='chicago_taxi_pipeline_simple',
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 testTaxiPipelineNewStyleCompatibility(self):
examples = external_input('/tmp/fake/path')
example_gen = CsvExampleGen(input=examples)
self.assertIs(example_gen.inputs['input'],
example_gen.inputs['input_base'])
statistics_gen = StatisticsGen(
examples=example_gen.outputs['examples'])
self.assertIs(statistics_gen.inputs['examples'],
statistics_gen.inputs['input_data'])
infer_schema = SchemaGen(
statistics=statistics_gen.outputs['statistics'])
self.assertIs(infer_schema.inputs['statistics'],
infer_schema.inputs['stats'])
self.assertIs(infer_schema.outputs['schema'],
infer_schema.outputs['output'])
validate_examples = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
self.assertIs(validate_examples.inputs['statistics'],
validate_examples.inputs['stats'])
self.assertIs(validate_examples.outputs['anomalies'],
validate_examples.outputs['output'])
transform = Transform(examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file='/tmp/fake/module/file')
self.assertIs(transform.inputs['examples'],
transform.inputs['input_data'])
self.assertIs(transform.outputs['transform_graph'],
transform.outputs['transform_output'])
trainer = Trainer(
module_file='/tmp/fake/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))
self.assertIs(trainer.inputs['transform_graph'],
trainer.inputs['transform_output'])
self.assertIs(trainer.outputs['model'], trainer.outputs['output'])
evaluator = 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'])
]))
self.assertIs(evaluator.inputs['model'],
evaluator.inputs['model_exports'])
model_validator = ModelValidator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
pusher = Pusher(model=trainer.outputs['output'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory='/fake/serving/dir')))
self.assertIs(pusher.inputs['model'], pusher.inputs['model_export'])
self.assertIs(pusher.outputs['pushed_model'],
pusher.outputs['model_push'])
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_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.csv_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=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 testCsvExampleGenOnDataflowRunner(self):
"""CsvExampleGen-only test pipeline on DataflowRunner invocation."""
pipeline_name = 'kubeflow-csv-example-gen-dataflow-test-{}'.format(
self._random_id())
pipeline = self._create_dataflow_pipeline(pipeline_name, [
CsvExampleGen(input=dsl_utils.csv_input(self._data_root)),
])
self._compile_and_run_pipeline(pipeline)
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) # Step 3
# Generates schema based on statistics files.
# schema_gen = 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=schema_gen.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=schema_gen.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=schema_gen.outputs.output, # Step 5
# transform_output=transform.outputs.transform_output, # Step 5
# train_steps=10000, # Step 5
# eval_steps=5000, # Step 5
# warm_starting=True) # 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
# Performs quality validation of a candidate model (compared to a baseline).
# model_validator = ModelValidator( # Step 7
# examples=example_gen.outputs.examples, 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
# serving_model_dir=serving_model_dir) # Step 7
return [
example_gen,
# statistics_gen, schema_gen, validate_stats, # Step 3
# transform, # Step 4
# trainer, # Step 5
# model_analyzer, # Step 6
# model_validator, pusher # Step 7
]
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_pipeline():
"""Implements the titanic taxi pipeline with TFX."""
examples = csv_input(data_dir)
# Brings data into the pipeline
example_gen = CsvExampleGen(input_base=examples)
return [example_gen]
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 _create_pipeline(pipeline_name: Text, pipeline_root: Text,
data_root: Text) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=data_root)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[example_gen],
enable_cache=True,
additional_pipeline_args={},
)
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='iris_training.csv'),
example_gen_pb2.Input.Split(name='eval', pattern='iris_test.csv')
])
example_gen = CsvExampleGen(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,
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
],
log_root='/var/tmp/tfx/logs',
)
def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
metadata_path: str) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=data_root)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[example_gen],
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,
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_base=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(input_data=example_gen.outputs['examples'])
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[example_gen, statistics_gen],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path))
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)
example_gen = CsvExampleGen(input=examples)
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'])
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs.output,
schema=infer_schema.outputs.output)
transform = Transform(
examples=example_gen.outputs['examples'],
schema=infer_schema.outputs['schema'],
module_file=module_file)
trainer = Trainer(
module_file=module_file,
examples=transform.outputs.transformed_examples,
schema=infer_schema.outputs.output,
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.output)
pusher = Pusher(
model=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],
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers]
)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text,
data_root: Text) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input_base=data_root)
# 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'])
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[example_gen, statistics_gen, infer_schema],
enable_cache=True,
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.
statistics_gen = StatisticsGen(input_data=example_gen.outputs.examples)
# Generates schema based on statistics files.
infer_schema = SchemaGen(stats=statistics_gen.outputs.output)
return pipeline.Pipeline(
pipeline_name='chicago_taxi_simple',
pipeline_root=_pipeline_root,
components=[example_gen, statistics_gen, infer_schema],
enable_cache=True,
metadata_db_root=_metadata_db_root,
)
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(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_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_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_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),
)
# Set the TFMA config for Model Evaluation and Validation.
eval_config = tfma.EvalConfig(
model_specs=[
# Using signature 'eval' implies the use of an EvalSavedModel. To use
# a serving model remove the signature to defaults to 'serving_default'
# and add a label_key.
tfma.ModelSpec(signature_name='eval')
],
metrics_specs=[
tfma.MetricsSpec(
# The metrics added here are in addition to those saved with the
# model (assuming either a keras model or EvalSavedModel is used).
# Any metrics added into the saved model (for example using
# model.compile(..., metrics=[...]), etc) will be computed
# automatically.
metrics=[tfma.MetricConfig(class_name='ExampleCount')],
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
thresholds={
'binary_accuracy':
tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.5}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
})
],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Data can be sliced along a feature column. In this case, data is
# sliced along feature column trip_start_hour.
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
eval_config=eval_config,
)
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=model_analyzer.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, pusher
],
enable_cache=enable_cache,
)
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_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_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)
example_gen = CsvExampleGen(input=examples)
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,
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
transformed_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))
model_resolver = ResolverNode(
instance_name='latest_blessed_model_resolver',
resolver_class=latest_blessed_model_resolver.
LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing))
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
thresholds={
'sparse_categorical_accuracy':
tfma.config.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.9}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
})
])
model_analyzer = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=model_analyzer.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_resolver, model_analyzer, pusher
],
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers],
enable_cache=True)
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),
)
# Get the latest blessed model for model validation.
model_resolver = ResolverNode(
instance_name='latest_blessed_model_resolver',
resolver_class=latest_blessed_model_resolver.
LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing))
# Uses TFMA to compute a evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(signature_name='eval')],
slicing_specs=[
tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
],
metrics_specs=[
tfma.MetricsSpec(
thresholds={
'binary_accuracy':
tfma.config.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
})
])
model_analyzer = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
# Change threshold will be ignored if there is no baseline (first run).
eval_config=eval_config)
# 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_analyzer.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_resolver, model_analyzer, pusher
],
# TODO(b/142684737): 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.
# 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},
)