def build_config(self, use_defaults=False):
# SLICING SPEC
slicing_specs = [tfma.SlicingSpec()]
if self.slices:
slicing_specs.extend([tfma.SlicingSpec(feature_keys=e)
for e in self.slices])
# MODEL SPEC
metric_labels = sorted(list(set(self.metrics.keys())))
model_specs = [tfma.ModelSpec(signature_name='zen_eval',
label_keys=self.output_mapping)]
# METRIC SPEC
baseline = [tfma.MetricConfig(class_name='ExampleCount')]
metrics_specs = []
for i, key in enumerate(metric_labels):
metrics = baseline.copy()
metrics.extend([tfma.MetricConfig(class_name=to_camel_case(m))
for m in self.metrics[key]])
metrics_specs.append(tfma.MetricsSpec(
output_names=[key],
metrics=metrics))
return tfma.EvalConfig(
model_specs=model_specs,
slicing_specs=slicing_specs,
metrics_specs=metrics_specs,
options=tfma.Options(
include_default_metrics=BoolValue(value=use_defaults)))
def build_config(self):
# SLICING SPEC
slicing_specs = [tfma.SlicingSpec()]
if self.slices:
slicing_specs.extend(
[tfma.SlicingSpec(feature_keys=e) for e in self.slices])
# MODEL SPEC
model_specs = [
tfma.ModelSpec(label_key=self.label_key,
prediction_key=self.prediction_key)
]
# METRIC SPEC
baseline = [tfma.MetricConfig(class_name='ExampleCount')]
for key in self.metrics:
baseline.append(tfma.MetricConfig(class_name=to_camel_case(key)))
metrics_specs = [tfma.MetricsSpec(metrics=baseline)]
return tfma.EvalConfig(
model_specs=model_specs,
slicing_specs=slicing_specs,
metrics_specs=metrics_specs,
options=tfma.Options(include_default_metrics=BoolValue(
value=False)))
def build(self, context: Context) -> BaseNode:
from tfx.components import Evaluator
import tensorflow_model_analysis as tfma
threshold = {
'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}))
}
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(signature_name='eval')],
slicing_specs=[
tfma.SlicingSpec(),
],
metrics_specs=[tfma.MetricsSpec(thresholds=threshold)])
component = Evaluator(
examples=context.get(self._config.inputs.examples),
model=context.get(self._config.inputs.model),
baseline_model=context.get(self._config.inputs.baseline_model),
eval_config=eval_config,
instance_name=context.abs_current_url_friendly)
put_outputs_to_context(context, self._config.outputs, component)
return component
def _get_eval_config() -> tfma.EvalConfig:
return tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key=LABEL_KEY)],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.01}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': 1e-10})))
])
])
def eval_metrics_spec(self, eval_accuracy_threshold):
class_name = (
"BinaryAccuracy"
if self.vocab_size + self.out_of_vocab_buckets == 2
else "SparseCategoricalAccuracy"
)
return tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name=class_name,
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": eval_accuracy_threshold}
),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-10},
),
),
)
]
)
def get_accuracy_eval_config(accuracy_threshold):
accuracy_threshold = tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': accuracy_threshold},
upper_bound={'value': 0.99}),
change_threshold=tfma.GenericChangeThreshold(
absolute={'value': 0.0001},
direction=tfma.MetricDirection.HIGHER_IS_BETTER))
metrics_specs = tfma.MetricsSpec(metrics=[
tfma.MetricConfig(class_name='BinaryAccuracy',
threshold=accuracy_threshold),
tfma.MetricConfig(class_name='ExampleCount')
])
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='income_bracket')],
metrics_specs=[metrics_specs],
slicing_specs=[
tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=['occupation'])
])
return eval_config
def get_eval_config():
model_specs = [
tfma.ModelSpec(signature_name='serving_default',
label_key='income_bracket',
example_weight_key='fnlwgt')
]
metrics_specs = [
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(class_name='BinaryAccuracy'),
tfma.MetricConfig(class_name='ExampleCount')
])
]
slicing_specs = [
tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=['occupation'])
]
eval_config = tfma.EvalConfig(model_specs=model_specs,
metrics_specs=metrics_specs,
slicing_specs=slicing_specs)
return eval_config
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
metadata_path: Text,
beam_pipeline_args: List[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)
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.
schema_gen = SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.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=schema_gen.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={
'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}))
})
])
evaluator = 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 a file destination if check passed.
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.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, schema_gen, example_validator, transform,
trainer, model_resolver, evaluator, pusher
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
beam_pipeline_args=beam_pipeline_args)
class ExecutorTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(('evaluation_w_eval_config', {
'eval_config':
proto_utils.proto_to_json(
tfma.EvalConfig(slicing_specs=[
tfma.SlicingSpec(feature_keys=['trip_start_hour']),
tfma.SlicingSpec(
feature_keys=['trip_start_day', 'trip_miles']),
]))
}), ('evaluation_w_module_file', {
'eval_config':
proto_utils.proto_to_json(
tfma.EvalConfig(slicing_specs=[
tfma.SlicingSpec(feature_keys=['trip_start_hour']),
tfma.SlicingSpec(
feature_keys=['trip_start_day', 'trip_miles']),
])),
'module_file':
None
}), ('evaluation_w_module_path', {
'eval_config':
proto_utils.proto_to_json(
tfma.EvalConfig(slicing_specs=[
tfma.SlicingSpec(feature_keys=['trip_start_hour']),
tfma.SlicingSpec(
feature_keys=['trip_start_day', 'trip_miles']),
])),
'module_path':
evaluator_module.__name__,
}))
def testEvalution(self, exec_properties):
source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
examples = standard_artifacts.Examples()
examples.uri = os.path.join(source_data_dir, 'csv_example_gen')
examples.split_names = artifact_utils.encode_split_names(['train', 'eval'])
model = standard_artifacts.Model()
baseline_model = standard_artifacts.Model()
model.uri = os.path.join(source_data_dir, 'trainer/current')
baseline_model.uri = os.path.join(source_data_dir, 'trainer/previous/')
schema = standard_artifacts.Schema()
schema.uri = os.path.join(source_data_dir, 'schema_gen')
input_dict = {
constants.EXAMPLES_KEY: [examples],
constants.MODEL_KEY: [model],
constants.SCHEMA_KEY: [schema],
}
# Create output dict.
eval_output = standard_artifacts.ModelEvaluation()
eval_output.uri = os.path.join(output_data_dir, 'eval_output')
blessing_output = standard_artifacts.ModelBlessing()
blessing_output.uri = os.path.join(output_data_dir, 'blessing_output')
output_dict = {
constants.EVALUATION_KEY: [eval_output],
constants.BLESSING_KEY: [blessing_output],
}
# Test multiple splits.
exec_properties[constants.EXAMPLE_SPLITS_KEY] = json_utils.dumps(
['train', 'eval'])
if 'module_file' in exec_properties:
exec_properties['module_file'] = os.path.join(source_data_dir,
'module_file',
'evaluator_module.py')
# Run executor.
evaluator = executor.Executor()
evaluator.Do(input_dict, output_dict, exec_properties)
# Check evaluator outputs.
self.assertTrue(
fileio.exists(os.path.join(eval_output.uri, 'eval_config.json')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'metrics')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'plots')))
self.assertFalse(
fileio.exists(os.path.join(blessing_output.uri, 'BLESSED')))
@parameterized.named_parameters(('legacy_feature_slicing', {
'feature_slicing_spec':
proto_utils.proto_to_json(
evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour']),
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_day', 'trip_miles']),
])),
}))
def testDoLegacySingleEvalSavedModelWFairness(self, exec_properties):
source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
examples = standard_artifacts.Examples()
examples.uri = os.path.join(source_data_dir, 'csv_example_gen')
examples.split_names = artifact_utils.encode_split_names(['train', 'eval'])
model = standard_artifacts.Model()
model.uri = os.path.join(source_data_dir, 'trainer/current')
input_dict = {
constants.EXAMPLES_KEY: [examples],
constants.MODEL_KEY: [model],
}
# Create output dict.
eval_output = standard_artifacts.ModelEvaluation()
eval_output.uri = os.path.join(output_data_dir, 'eval_output')
blessing_output = standard_artifacts.ModelBlessing()
blessing_output.uri = os.path.join(output_data_dir, 'blessing_output')
output_dict = {
constants.EVALUATION_KEY: [eval_output],
constants.BLESSING_KEY: [blessing_output],
}
try:
# Need to import the following module so that the fairness indicator
# post-export metric is registered. This may raise an ImportError if the
# currently-installed version of TFMA does not support fairness
# indicators.
import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable
exec_properties['fairness_indicator_thresholds'] = [
0.1, 0.3, 0.5, 0.7, 0.9
]
except ImportError:
logging.warning(
'Not testing fairness indicators because a compatible TFMA version '
'is not installed.')
# List needs to be serialized before being passed into Do function.
exec_properties[constants.EXAMPLE_SPLITS_KEY] = json_utils.dumps(None)
# Run executor.
evaluator = executor.Executor()
evaluator.Do(input_dict, output_dict, exec_properties)
# Check evaluator outputs.
self.assertTrue(
fileio.exists(os.path.join(eval_output.uri, 'eval_config.json')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'metrics')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'plots')))
self.assertFalse(
fileio.exists(os.path.join(blessing_output.uri, 'BLESSED')))
@parameterized.named_parameters(
(
'eval_config_w_validation',
{
'eval_config':
proto_utils.proto_to_json(
tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(label_key='tips'),
],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.config.MetricConfig(
class_name='ExampleCount',
# Count > 0, OK.
threshold=tfma.config.MetricThreshold(
value_threshold=tfma
.GenericValueThreshold(
lower_bound={'value': 0}))),
]),
],
slicing_specs=[tfma.SlicingSpec()]))
},
True,
True),
(
'eval_config_w_validation_fail',
{
'eval_config':
proto_utils.proto_to_json(
tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
name='baseline1',
label_key='tips',
is_baseline=True),
tfma.ModelSpec(
name='candidate1', label_key='tips'),
],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.config.MetricConfig(
class_name='ExampleCount',
# Count < -1, NOT OK.
threshold=tfma.config.MetricThreshold(
value_threshold=tfma
.GenericValueThreshold(
upper_bound={'value': -1}))),
]),
],
slicing_specs=[tfma.SlicingSpec()]))
},
False,
True),
(
'no_baseline_model_ignore_change_threshold_validation_pass',
{
'eval_config':
proto_utils.proto_to_json(
tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
name='baseline',
label_key='tips',
is_baseline=True),
tfma.ModelSpec(
name='candidate', label_key='tips'),
],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.config.MetricConfig(
class_name='ExampleCount',
# Count > 0, OK.
threshold=tfma.config.MetricThreshold(
value_threshold=tfma
.GenericValueThreshold(
lower_bound={'value': 0}))),
tfma.config.MetricConfig(
class_name='Accuracy',
# Should be ignored due to no baseline.
threshold=tfma.config.MetricThreshold(
change_threshold=tfma
.GenericChangeThreshold(
relative={'value': 0},
direction=tfma.MetricDirection
.LOWER_IS_BETTER))),
]),
],
slicing_specs=[tfma.SlicingSpec()]))
},
True,
False))
def testDoValidation(self, exec_properties, blessed, has_baseline):
source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
# Create input dict.
examples = standard_artifacts.Examples()
examples.uri = os.path.join(source_data_dir, 'csv_example_gen')
examples.split_names = artifact_utils.encode_split_names(['train', 'eval'])
model = standard_artifacts.Model()
baseline_model = standard_artifacts.Model()
model.uri = os.path.join(source_data_dir, 'trainer/current')
baseline_model.uri = os.path.join(source_data_dir, 'trainer/previous/')
blessing_output = standard_artifacts.ModelBlessing()
blessing_output.uri = os.path.join(output_data_dir, 'blessing_output')
schema = standard_artifacts.Schema()
schema.uri = os.path.join(source_data_dir, 'schema_gen')
input_dict = {
constants.EXAMPLES_KEY: [examples],
constants.MODEL_KEY: [model],
constants.SCHEMA_KEY: [schema],
}
if has_baseline:
input_dict[constants.BASELINE_MODEL_KEY] = [baseline_model]
# Create output dict.
eval_output = standard_artifacts.ModelEvaluation()
eval_output.uri = os.path.join(output_data_dir, 'eval_output')
blessing_output = standard_artifacts.ModelBlessing()
blessing_output.uri = os.path.join(output_data_dir, 'blessing_output')
output_dict = {
constants.EVALUATION_KEY: [eval_output],
constants.BLESSING_KEY: [blessing_output],
}
# List needs to be serialized before being passed into Do function.
exec_properties[constants.EXAMPLE_SPLITS_KEY] = json_utils.dumps(None)
# Run executor.
evaluator = executor.Executor()
evaluator.Do(input_dict, output_dict, exec_properties)
# Check evaluator outputs.
self.assertTrue(
fileio.exists(os.path.join(eval_output.uri, 'eval_config.json')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'metrics')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'plots')))
self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'validations')))
if blessed:
self.assertTrue(
fileio.exists(os.path.join(blessing_output.uri, 'BLESSED')))
else:
self.assertTrue(
fileio.exists(os.path.join(blessing_output.uri, 'NOT_BLESSED')))
def _create_pipeline(pipeline_name: Text, pipeline_root: Text,
training_data_root: Text, inference_data_root: Text,
module_file: Text, metadata_path: Text,
beam_pipeline_args: List[Text]) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
# Brings training data into the pipeline or otherwise joins/converts
# training data.
training_example_gen = CsvExampleGen(input_base=training_data_root,
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.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=training_example_gen.outputs['examples'],
schema=schema_gen.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=schema_gen.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={
'accuracy':
tfma.config.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
})
])
evaluator = Evaluator(examples=training_example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
# Brings inference data into the pipeline.
inference_example_gen = CsvExampleGen(
input_base=inference_data_root,
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=trainer.outputs['model'],
model_blessing=evaluator.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,
schema_gen, example_validator, transform, trainer, model_resolver,
evaluator, bulk_inferrer
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
beam_pipeline_args=beam_pipeline_args)
def _create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_root: Text,
trainer_module_file: Text,
evaluator_module_file: Text,
serving_model_dir: Text,
metadata_path: Text,
beam_pipeline_args: List[Text],
) -> tfx.dsl.Pipeline:
"""Implements the Penguin pipeline with TFX."""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = tfx.components.CsvExampleGen(
input_base=os.path.join(data_root, 'labelled'))
# Computes statistics over data for visualization and example validation.
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# TODO(humichael): Handle applying transformation component in Milestone 3.
# Uses user-provided Python function that trains a model using TF-Learn.
# Num_steps is not provided during evaluation because the scikit-learn model
# loads and evaluates the entire test set at once.
trainer = tfx.components.Trainer(
module_file=trainer_module_file,
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
train_args=tfx.proto.TrainArgs(num_steps=2000),
eval_args=tfx.proto.EvalArgs())
# Get the latest blessed model for model validation.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
# Uses TFMA to compute evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='Accuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = tfx.components.Evaluator(
module_file=evaluator_module_file,
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir)))
return tfx.dsl.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen,
statistics_gen,
schema_gen,
example_validator,
trainer,
model_resolver,
evaluator,
pusher,
],
enable_cache=True,
metadata_connection_config=tfx.orchestration.metadata.
sqlite_metadata_connection_config(metadata_path),
beam_pipeline_args=beam_pipeline_args,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
preprocessing_fn: Text,
run_fn: Text,
train_args: trainer_pb2.TrainArgs,
eval_args: trainer_pb2.EvalArgs,
eval_accuracy_threshold: float,
serving_model_dir: Text,
metadata_connection_config: Optional[
metadata_store_pb2.ConnectionConfig] = None,
beam_pipeline_args: Optional[List[Text]] = None,
) -> pipeline.Pipeline:
"""Implements the penguin pipeline with TFX."""
components = []
# Brings data into the pipeline or otherwise joins/converts training data.
# TODO(step 2): Might use another ExampleGen class for your data.
example_gen = CsvExampleGen(input_base=data_path)
components.append(example_gen)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
components.append(statistics_gen)
# Generates schema based on statistics files.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
components.append(schema_gen)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator( # pylint: disable=unused-variable
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
components.append(example_validator)
# Performs transformations and feature engineering in training and serving.
transform = Transform( # pylint: disable=unused-variable
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)
# TODO(step 3): Uncomment here to add Transform to the pipeline.
# components.append(transform)
# Uses user-provided Python function that implements a model using Tensorflow.
trainer = Trainer(
run_fn=run_fn,
examples=example_gen.outputs['examples'],
# Use outputs of Transform as training inputs if Transform is used.
# examples=transform.outputs['transformed_examples'],
# transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=train_args,
eval_args=eval_args)
# TODO(step 4): Uncomment here to add Trainer to the pipeline.
# components.append(trainer)
# Get the latest blessed model for model validation.
model_resolver = resolver.Resolver(
strategy_class=latest_blessed_model_resolver.
LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(
type=ModelBlessing)).with_id('latest_blessed_model_resolver')
# TODO(step 5): Uncomment here to add Resolver to the pipeline.
# components.append(model_resolver)
# 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(label_key=features.LABEL_KEY)],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': eval_accuracy_threshold}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = Evaluator( # pylint: disable=unused-variable
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)
# TODO(step 5): Uncomment here to add Evaluator to the pipeline.
# components.append(evaluator)
# Pushes the model to a file destination if check passed.
pusher = Pusher( # pylint: disable=unused-variable
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
# TODO(step 5): Uncomment here to add Pusher to the pipeline.
# components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
# Change this value to control caching of execution results. Default value
# is `False`.
# enable_cache=True,
metadata_connection_config=metadata_connection_config,
beam_pipeline_args=beam_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.
"""
example_gen = CsvExampleGen(input_base=csv_input_location)
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=transform_module)
latest_model_resolver = resolver.Resolver(
strategy_class=latest_artifact_strategy.LatestArtifactStrategy,
latest_model=Channel(type=Model)).with_id('latest_model_resolver')
trainer = Trainer(
transformed_examples=transform.outputs['transformed_examples'],
schema=schema_gen.outputs['schema'],
base_model=latest_model_resolver.outputs['latest_model'],
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,
)
# Set the TFMA config for Model Evaluation and Validation.
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(signature_name='eval')],
metrics_specs=[
tfma.MetricsSpec(
metrics=[tfma.MetricConfig(class_name='ExampleCount')],
thresholds={
'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=[
tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
evaluator = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
eval_config=eval_config)
infra_validator = InfraValidator(
model=trainer.outputs['model'],
examples=example_gen.outputs['examples'],
serving_spec=infra_validator_pb2.ServingSpec(
tensorflow_serving=infra_validator_pb2.TensorFlowServing(
tags=['latest']),
kubernetes=infra_validator_pb2.KubernetesConfig()),
request_spec=infra_validator_pb2.RequestSpec(
tensorflow_serving=infra_validator_pb2.
TensorFlowServingRequestSpec()))
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.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,
schema_gen,
example_validator,
transform,
latest_model_resolver,
trainer,
evaluator,
infra_validator,
pusher,
]
def _create_pipeline(pipeline_name: Text,
pipeline_root: Text) -> pipeline.Pipeline:
"""Implements the Iris flowers pipeline with TFX."""
examples = external_input(_data_root_param)
# 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'])
# 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_param)
# Uses user-provided Python function that implements a model using Keras.
trainer = Trainer(
module_file=_module_file_param,
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=infer_schema.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=100),
eval_args=trainer_pb2.EvalArgs(num_steps=50))
# 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 an evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
# Note: to compile this successfully you'll need TFMA at >= 0.21.5
eval_config = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(name='candidate', label_key='variety'),
tfma.ModelSpec(name='baseline',
label_key='variety',
is_baseline=True)
],
slicing_specs=[
tfma.SlicingSpec(),
# Data can be sliced along a feature column. Required by TFMA visualization.
tfma.SlicingSpec(feature_keys=['sepal_length'])
],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=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})))
])
])
# Uses TFMA to compute a evaluation statistics over features of a model.
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 a file destination 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=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_resolver, model_analyzer, pusher
],
enable_cache=True,
)
def create_test_pipeline():
"""Builds an Iris example pipeline with slight changes."""
pipeline_name = "iris"
iris_root = "iris_root"
serving_model_dir = os.path.join(iris_root, "serving_model", pipeline_name)
tfx_root = "tfx_root"
data_path = os.path.join(tfx_root, "data_path")
pipeline_root = os.path.join(tfx_root, "pipelines", pipeline_name)
example_gen = CsvExampleGen(input_base=data_path)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
importer = ImporterNode(instance_name="my_importer",
source_uri="m/y/u/r/i",
properties={
"split_names": "['train', 'eval']",
},
custom_properties={
"int_custom_property": 42,
"str_custom_property": "42",
},
artifact_type=standard_artifacts.Examples)
schema_gen = SchemaGen(statistics=statistics_gen.outputs["statistics"],
infer_feature_shape=True)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"])
trainer = Trainer(
# Use RuntimeParameter as module_file to test out RuntimeParameter in
# compiler.
module_file=data_types.RuntimeParameter(name="module_file",
default=os.path.join(
iris_root,
"iris_utils.py"),
ptype=str),
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
examples=example_gen.outputs["examples"],
schema=schema_gen.outputs["schema"],
train_args=trainer_pb2.TrainArgs(num_steps=2000),
# Attaching `TrainerArgs` as platform config is not sensible practice,
# but is only for testing purpose.
eval_args=trainer_pb2.EvalArgs(num_steps=5)).with_platform_config(
config=trainer_pb2.TrainArgs(num_steps=2000))
model_resolver = ResolverNode(
instance_name="latest_blessed_model_resolver",
resolver_class=latest_blessed_model_resolver.
LatestBlessedModelResolver,
baseline_model=Channel(type=standard_artifacts.Model,
producer_component_id="Trainer"),
# Cannot add producer_component_id="Evaluator" for model_blessing as it
# raises "producer component should have already been compiled" error.
model_blessing=Channel(type=standard_artifacts.ModelBlessing))
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(signature_name="eval")],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
thresholds={
"sparse_categorical_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}))
})
])
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["baseline_model"],
eval_config=eval_config)
pusher = Pusher(model=trainer.outputs["model"],
model_blessing=evaluator.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,
importer,
schema_gen,
example_validator,
trainer,
model_resolver,
evaluator,
pusher,
],
enable_cache=False,
beam_pipeline_args=["--my_testing_beam_pipeline_args=bar"],
# Attaching `TrainerArgs` as platform config is not sensible practice,
# but is only for testing purpose.
platform_config=trainer_pb2.TrainArgs(num_steps=2000),
execution_mode=pipeline.ExecutionMode.ASYNC)
def init_components(data_dir,
module_file,
serving_model_dir=None,
ai_platform_training_args=None,
ai_platform_serving_args=None,
training_steps=1000,
eval_steps=200):
"""
This function is to initialize tfx components
"""
if serving_model_dir and ai_platform_serving_args:
raise NotImplementedError(
"Can't set ai_platform_serving_args and serving_model_dir at "
"the same time. Choose one deployment option.")
output = example_gen_pb2.Output(split_config=example_gen_pb2.SplitConfig(
splits=[
example_gen_pb2.SplitConfig.Split(name="train", hash_buckets=99),
example_gen_pb2.SplitConfig.Split(name="eval", hash_buckets=1),
]))
example_gen = CsvExampleGen(input_base=data_dir, output_config=output)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
schema_gen = SchemaGen(
statistics=statistics_gen.outputs["statistics"],
infer_feature_shape=False,
)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
transform = Transform(
examples=example_gen.outputs["examples"],
schema=schema_gen.outputs["schema"],
module_file=module_file,
)
training_kwargs = {
"module_file": module_file,
"examples": transform.outputs["transformed_examples"],
"schema": schema_gen.outputs["schema"],
"transform_graph": transform.outputs['transform_graph'],
"train_args": trainer_pb2.TrainArgs(num_steps=training_steps),
"eval_args": trainer_pb2.EvalArgs(num_steps=eval_steps),
}
if ai_platform_training_args:
training_kwargs.update({
"custom_executor_spec":
executor_spec.ExecutorClassSpec(
aip_trainer_executor.GenericExecutor),
"custom_config": {
aip_trainer_executor.TRAINING_ARGS_KEY:
ai_platform_training_args # noqa
},
})
else:
training_kwargs.update({
"custom_executor_spec":
executor_spec.ExecutorClassSpec(GenericExecutor)
})
trainer = Trainer(**training_kwargs)
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),
)
#model_resolver for tfx==0.30.0
# model_resolver = tfx.dsl.Resolver(
# strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
# model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
# model_blessing=tfx.dsl.Channel(
# type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
# 'latest_blessed_model_resolver')
#the book's eval_config might be wrong,
#threshold has to be set within the tfma.MetricConfig() with each metric
#this seems to have caused the models not be blessed
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key="consumer_disputed")],
slicing_specs=[
tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=["product"]),
],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(class_name='ExampleCount'),
tfma.MetricConfig(
class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.5}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": 0.01},
),
)),
# tfma.MetricConfig(
# class_name='AUC',
# threshold=tfma.MetricThreshold(
# value_threshold=tfma.GenericValueThreshold(
# lower_bound={'value': 0.5}
# ),
# change_threshold=tfma.GenericChangeThreshold(
# direction=tfma.MetricDirection.HIGHER_IS_BETTER,
# absolute={"value": 0.01},
# ),
# )
# ),
])
],
)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
# baseline_model=model_resolver.outputs["model"],
eval_config=eval_config,
)
pusher_kwargs = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
}
if ai_platform_serving_args:
pusher_kwargs.update({
"custom_executor_spec":
executor_spec.ExecutorClassSpec(aip_pusher_executor.Executor),
"custom_config": {
aip_pusher_executor.SERVING_ARGS_KEY:
ai_platform_serving_args # noqa
},
})
elif serving_model_dir:
pusher_kwargs.update({
"push_destination":
pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir))
})
else:
raise NotImplementedError(
"Provide ai_platform_serving_args or serving_model_dir.")
pusher = Pusher(**pusher_kwargs)
#compile all components in a list
components = [
example_gen,
statistics_gen,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
pusher,
]
return components
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
module_file: Text,
ai_platform_training_args: Dict[Text, Text],
ai_platform_serving_args: Dict[Text, Text],
beam_pipeline_args: Optional[List[Text]] = None) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines.
Args:
pipeline_name: name of the TFX pipeline being created.
pipeline_root: root directory of the pipeline. Should be a valid GCS path.
module_file: uri of the module files used in Trainer and Transform
components.
ai_platform_training_args: Args of CAIP training job. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
for detailed description.
ai_platform_serving_args: Args of CAIP model deployment. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
for detailed description.
beam_pipeline_args: Optional list of beam pipeline options. Please refer to
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options.
When this argument is not provided, the default is to use GCP
DataflowRunner with 50GB disk size as specified in this function. If an
empty list is passed in, default specified by Beam will be used, which can
be found at
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options
Returns:
A TFX pipeline object.
"""
# The rate at which to sample rows from the Taxi dataset using BigQuery.
# The full taxi dataset is > 200M record. In the interest of resource
# savings and time, we've set the default for this example to be much smaller.
# Feel free to crank it up and process the full dataset!
# By default it generates a 0.1% random sample.
query_sample_rate = data_types.RuntimeParameter(name='query_sample_rate',
ptype=float,
default=0.001)
# This is the upper bound of FARM_FINGERPRINT in Bigquery (ie the max value of
# signed int64).
max_int64 = '0x7FFFFFFFFFFFFFFF'
# The query that extracts the examples from BigQuery. The Chicago Taxi dataset
# used for this example is a public dataset available on Google AI Platform.
# https://console.cloud.google.com/marketplace/details/city-of-chicago-public-data/chicago-taxi-trips
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=str(query_sample_rate))
# Beam args to run data processing on DataflowRunner.
# TODO(b/151114974): Remove `disk_size_gb` flag after default is increased.
# TODO(b/151116587): Remove `shuffle_mode` flag after default is changed.
if beam_pipeline_args is None:
beam_pipeline_args = [
'--runner=DataflowRunner',
'--experiments=shuffle_mode=auto',
'--project=' + _project_id,
'--temp_location=' + os.path.join(_output_bucket, 'tmp'),
'--region=' + _gcp_region,
'--disk_size_gb=50',
]
# Number of epochs in training.
train_steps = data_types.RuntimeParameter(
name='train_steps',
default=10000,
ptype=int,
)
# Number of epochs in evaluation.
eval_steps = data_types.RuntimeParameter(
name='eval_steps',
default=5000,
ptype=int,
)
# 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.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
# Update ai_platform_training_args if distributed training was enabled.
# Number of worker machines used in distributed training.
worker_count = data_types.RuntimeParameter(
name='worker_count',
default=2,
ptype=int,
)
# Type of worker machines used in distributed training.
worker_type = data_types.RuntimeParameter(
name='worker_type',
default='standard',
ptype=str,
)
local_training_args = copy.deepcopy(ai_platform_training_args)
if FLAGS.distributed_training:
local_training_args.update({
# You can specify the machine types, the number of replicas for workers
# and parameter servers.
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#ScaleTier
'scaleTier': 'CUSTOM',
'masterType': 'large_model',
'workerType': worker_type,
'parameterServerType': 'standard',
'workerCount': worker_count,
'parameterServerCount': 1
})
# 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=schema_gen.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
custom_config={
ai_platform_trainer_executor.TRAINING_ARGS_KEY: local_training_args
})
# 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}))
})
])
evaluator = 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(custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_pusher_executor.Executor),
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
custom_config={
ai_platform_pusher_executor.SERVING_ARGS_KEY:
ai_platform_serving_args
})
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, schema_gen, example_validator,
transform, trainer, model_resolver, evaluator, pusher
],
beam_pipeline_args=beam_pipeline_args,
)
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],
enable_tuning: bool,
beam_pipeline_args: Optional[List[Text]] = None) -> pipeline.Pipeline:
"""Implements the Iris flowers pipeline with TFX and Kubeflow Pipeline.
Args:
pipeline_name: name of the TFX pipeline being created.
pipeline_root: root directory of the pipeline. Should be a valid GCS path.
data_root: uri of the Iris flowers data.
module_file: uri of the module files used in Trainer and Transform
components.
ai_platform_training_args: Args of CAIP training job. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
for detailed description.
ai_platform_serving_args: Args of CAIP model deployment. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
for detailed description.
enable_tuning: If True, the hyperparameter tuning through CloudTuner is
enabled.
beam_pipeline_args: Optional list of beam pipeline options. Please refer to
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options.
When this argument is not provided, the default is to use GCP
DataflowRunner with 50GB disk size as specified in this function. If an
empty list is passed in, default specified by Beam will be used, which can
be found at
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options
Returns:
A TFX pipeline object.
"""
examples = external_input(data_root)
# Beam args to run data processing on DataflowRunner.
#
# TODO(b/151114974): Remove `disk_size_gb` flag after default is increased.
# TODO(b/151116587): Remove `shuffle_mode` flag after default is changed.
# TODO(b/156874687): Remove `machine_type` after IP addresses are no longer a
# scaling bottleneck.
if beam_pipeline_args is None:
beam_pipeline_args = [
'--runner=DataflowRunner',
'--project=' + _project_id,
'--temp_location=' + os.path.join(_output_bucket, 'tmp'),
'--region=' + _gcp_region,
# Temporary overrides of defaults.
'--disk_size_gb=50',
'--experiments=shuffle_mode=auto',
'--machine_type=n1-standard-8',
]
# Number of epochs in training.
train_steps = data_types.RuntimeParameter(
name='train_steps',
default=100,
ptype=int,
)
# Number of epochs in evaluation.
eval_steps = data_types.RuntimeParameter(
name='eval_steps',
default=50,
ptype=int,
)
# 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.
schema_gen = SchemaGen(
statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
# Update ai_platform_training_args if distributed training was enabled.
# Number of worker machines used in distributed training.
worker_count = data_types.RuntimeParameter(
name='worker_count',
default=2,
ptype=int,
)
# Type of worker machines used in distributed training.
worker_type = data_types.RuntimeParameter(
name='worker_type',
default='standard',
ptype=str,
)
local_training_args = copy.deepcopy(ai_platform_training_args)
if FLAGS.distributed_training:
local_training_args.update({
# You can specify the machine types, the number of replicas for workers
# and parameter servers.
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#ScaleTier
'scaleTier': 'CUSTOM',
'masterType': 'large_model',
'workerType': worker_type,
'parameterServerType': 'standard',
'workerCount': worker_count,
'parameterServerCount': 1,
})
# Tunes the hyperparameters for model training based on user-provided Python
# function. Note that once the hyperparameters are tuned, you can drop the
# Tuner component from pipeline and feed Trainer with tuned hyperparameters.
if enable_tuning:
# The Tuner component launches 1 AIP Training job for flock management.
# For example, 3 workers (defined by num_parallel_trials) in the flock
# management AIP Training job, each runs Tuner.Executor.
# Then, 3 AIP Training Jobs (defined by local_training_args) are invoked
# from each worker in the flock management Job for Trial execution.
tuner = Tuner(
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
tune_args=tuner_pb2.TuneArgs(
# num_parallel_trials=3 means that 3 search loops are
# running in parallel.
# Each tuner may include a distributed training job which can be
# specified in local_training_args above (e.g. 1 PS + 2 workers).
num_parallel_trials=3),
custom_config={
# Configures Cloud AI Platform-specific configs . For details, see
# https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#traininginput.
ai_platform_trainer_executor.TRAINING_ARGS_KEY:
local_training_args
})
# Uses user-provided Python function that trains a model.
trainer = Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_trainer_executor.GenericExecutor),
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
# If Tuner is in the pipeline, Trainer can take Tuner's output
# best_hyperparameters artifact as input and utilize it in the user module
# code.
#
# If there isn't Tuner in the pipeline, either use ImporterNode to import
# a previous Tuner's output to feed to Trainer, or directly use the tuned
# hyperparameters in user module code and set hyperparameters to None
# here.
#
# Example of ImporterNode,
# hparams_importer = ImporterNode(
# instance_name='import_hparams',
# source_uri='path/to/best_hyperparameters.txt',
# artifact_type=HyperParameters)
# ...
# hyperparameters = hparams_importer.outputs['result'],
hyperparameters=(tuner.outputs['best_hyperparameters']
if enable_tuning else None),
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
custom_config={
ai_platform_trainer_executor.TRAINING_ARGS_KEY:
local_training_args
})
# 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 an evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='variety')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = 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)
pusher = Pusher(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_pusher_executor.Executor),
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
custom_config={
ai_platform_pusher_executor.SERVING_ARGS_KEY: ai_platform_serving_args
})
components = [
example_gen,
statistics_gen,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
pusher,
]
if enable_tuning:
components.append(tuner)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
beam_pipeline_args=beam_pipeline_args)
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],
enable_tuning: bool,
beam_pipeline_args: List[Text],
) -> tfx.dsl.Pipeline:
"""Implements the penguin pipeline with TFX and Kubeflow Pipeline.
Args:
pipeline_name: name of the TFX pipeline being created.
pipeline_root: root directory of the pipeline. Should be a valid GCS path.
data_root: uri of the penguin data.
module_file: uri of the module files used in Trainer and Transform
components.
ai_platform_training_args: Args of CAIP training job. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
for detailed description.
ai_platform_serving_args: Args of CAIP model deployment. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
for detailed description.
enable_tuning: If True, the hyperparameter tuning through CloudTuner is
enabled.
beam_pipeline_args: List of beam pipeline options. Please refer to
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options.
Returns:
A TFX pipeline object.
"""
# Number of epochs in training.
train_steps = tfx.dsl.RuntimeParameter(
name='train_steps',
default=100,
ptype=int,
)
# Number of epochs in evaluation.
eval_steps = tfx.dsl.RuntimeParameter(
name='eval_steps',
default=50,
ptype=int,
)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = tfx.components.CsvExampleGen(
input_base=os.path.join(data_root, 'labelled'))
# Computes statistics over data for visualization and example validation.
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = tfx.components.Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
# Tunes the hyperparameters for model training based on user-provided Python
# function. Note that once the hyperparameters are tuned, you can drop the
# Tuner component from pipeline and feed Trainer with tuned hyperparameters.
if enable_tuning:
# The Tuner component launches 1 AIP Training job for flock management of
# parallel tuning. For example, 2 workers (defined by num_parallel_trials)
# in the flock management AIP Training job, each runs a search loop for
# trials as shown below.
# Tuner component -> CAIP job X -> CloudTunerA -> tuning trials
# -> CloudTunerB -> tuning trials
#
# Distributed training for each trial depends on the Tuner
# (kerastuner.BaseTuner) setup in tuner_fn. Currently CloudTuner is single
# worker training per trial. DistributingCloudTuner (a subclass of
# CloudTuner) launches remote distributed training job per trial.
#
# E.g., single worker training per trial
# ... -> CloudTunerA -> single worker training
# -> CloudTunerB -> single worker training
# vs distributed training per trial
# ... -> DistributingCloudTunerA -> CAIP job Y -> master,worker1,2,3
# -> DistributingCloudTunerB -> CAIP job Z -> master,worker1,2,3
tuner = tfx.extensions.google_cloud_ai_platform.Tuner(
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
tune_args=tfx.proto.TuneArgs(
# num_parallel_trials=3 means that 3 search loops are
# running in parallel.
num_parallel_trials=3),
custom_config={
# Note that this TUNING_ARGS_KEY will be used to start the CAIP job
# for parallel tuning (CAIP job X above).
#
# num_parallel_trials will be used to fill/overwrite the
# workerCount specified by TUNING_ARGS_KEY:
# num_parallel_trials = workerCount + 1 (for master)
tfx.extensions.google_cloud_ai_platform.experimental.TUNING_ARGS_KEY:
ai_platform_training_args,
# This working directory has to be a valid GCS path and will be used
# to launch remote training job per trial.
tfx.extensions.google_cloud_ai_platform.experimental.REMOTE_TRIALS_WORKING_DIR_KEY:
os.path.join(_pipeline_root, 'trials'),
})
# Uses user-provided Python function that trains a model.
trainer = tfx.extensions.google_cloud_ai_platform.Trainer(
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
# If Tuner is in the pipeline, Trainer can take Tuner's output
# best_hyperparameters artifact as input and utilize it in the user module
# code.
#
# If there isn't Tuner in the pipeline, either use ImporterNode to import
# a previous Tuner's output to feed to Trainer, or directly use the tuned
# hyperparameters in user module code and set hyperparameters to None
# here.
#
# Example of ImporterNode,
# hparams_importer = ImporterNode(
# source_uri='path/to/best_hyperparameters.txt',
# artifact_type=HyperParameters).with_id('import_hparams')
# ...
# hyperparameters = hparams_importer.outputs['result'],
hyperparameters=(tuner.outputs['best_hyperparameters']
if enable_tuning else None),
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
custom_config={
tfx.extensions.google_cloud_ai_platform.TRAINING_ARGS_KEY:
ai_platform_training_args
})
# Get the latest blessed model for model validation.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
# Uses TFMA to compute evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
pusher = tfx.extensions.google_cloud_ai_platform.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
custom_config={
tfx.extensions.google_cloud_ai_platform.experimental.PUSHER_SERVING_ARGS_KEY:
ai_platform_serving_args
})
components = [
example_gen,
statistics_gen,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
pusher,
]
if enable_tuning:
components.append(tuner)
return tfx.dsl.Pipeline(pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
beam_pipeline_args=beam_pipeline_args)
def _create_pipeline(
pipeline_root: str,
csv_input_location: str,
taxi_module_file: tfx.dsl.experimental.RuntimeParameter,
push_destination: tfx.dsl.experimental.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.
"""
example_gen = tfx.components.CsvExampleGen(input_base=csv_input_location)
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False,
)
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'],
)
transform = tfx.components.Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=taxi_module_file,
)
trainer = tfx.components.Trainer(
module_file=taxi_module_file,
examples=transform.outputs['transformed_examples'],
schema=schema_gen.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=tfx.proto.TrainArgs(num_steps=10),
eval_args=tfx.proto.EvalArgs(num_steps=5),
)
# Set the TFMA config for Model Evaluation and Validation.
eval_config = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name='serving_default',
label_key='tips_xf',
preprocessing_function_names=['transform_features'])
],
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'])
])
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
eval_config=eval_config,
)
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=push_destination,
)
return tfx.dsl.Pipeline(
pipeline_name='parameterized_tfx_oss',
pipeline_root=pipeline_root,
components=[
example_gen, statistics_gen, schema_gen, example_validator,
transform, trainer, evaluator, 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) -> 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.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that trains a model using TF-Learn.
trainer = Trainer(
module_file=module_file,
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=2000),
eval_args=trainer_pb2.EvalArgs(num_steps=5))
# 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 an evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='variety')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = 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)
# Performs infra validation of a candidate model to prevent unservable model
# from being pushed. This config will launch a model server of the latest
# TensorFlow Serving image in a local docker engine.
infra_validator = InfraValidator(
model=trainer.outputs['model'],
examples=example_gen.outputs['examples'],
serving_spec=infra_validator_pb2.ServingSpec(
tensorflow_serving=infra_validator_pb2.TensorFlowServing(
tags=['latest']),
local_docker=infra_validator_pb2.LocalDockerConfig()),
request_spec=infra_validator_pb2.RequestSpec(
tensorflow_serving=infra_validator_pb2.
TensorFlowServingRequestSpec()))
# 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=evaluator.outputs['blessing'],
infra_blessing=infra_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,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
infra_validator,
pusher,
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
# TODO(b/142684737): The multi-processing API might change.
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers],
)
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={
'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'])
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
metadata_path: Text,
beam_pipeline_args: List[Text]) -> pipeline.Pipeline:
"""Implements the imdb sentiment analysis pipline with TFX."""
output = example_gen_pb2.Output(split_config=example_gen_pb2.SplitConfig(
splits=[
example_gen_pb2.SplitConfig.Split(name='train', hash_buckets=9),
example_gen_pb2.SplitConfig.Split(name='eval', hash_buckets=1)
]))
# Brings data in to the pipline
example_gen = CsvExampleGen(input_base=data_root, output_config=output)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
# Uses user-provided Python function that trains a model.
trainer = Trainer(module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=500),
eval_args=trainer_pb2.EvalArgs(num_steps=200))
# 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 evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='label')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
# Increase this threshold when training on complete
# dataset.
lower_bound={'value': 0.01}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-2})))
])
])
evaluator = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)))
components = [
example_gen,
statistics_gen,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
pusher,
]
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
enable_cache=True,
beam_pipeline_args=beam_pipeline_args)
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
beam_pipeline_args: List[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 = 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.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.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,
custom_executor_spec=executor_spec.ExecutorClassSpec(Executor),
transformed_examples=transform.outputs['transformed_examples'],
schema=schema_gen.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 = resolver.Resolver(
strategy_class=latest_blessed_model_resolver.
LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(
type=ModelBlessing)).with_id('latest_blessed_model_resolver')
# 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={
'accuracy':
tfma.config.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
})
])
evaluator = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
# Performs infra validation of a candidate model to prevent unservable model
# from being pushed. In order to use InfraValidator component, persistent
# volume and its claim that the pipeline is using should be a ReadWriteMany
# access mode.
infra_validator = InfraValidator(
model=trainer.outputs['model'],
examples=example_gen.outputs['examples'],
serving_spec=infra_validator_pb2.ServingSpec(
tensorflow_serving=infra_validator_pb2.TensorFlowServing(
tags=['latest']),
kubernetes=infra_validator_pb2.KubernetesConfig()),
request_spec=infra_validator_pb2.RequestSpec(
tensorflow_serving=infra_validator_pb2.
TensorFlowServingRequestSpec()))
# 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=evaluator.outputs['blessing'],
infra_blessing=infra_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,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
infra_validator,
pusher,
],
beam_pipeline_args=beam_pipeline_args)
def generate_pipeline(pipeline_name, pipeline_root, train_data, test_data,
train_steps, eval_steps, pusher_target, runner):
module_file = 'util.py' # util.py is a file in the same folder
# RuntimeParameter is only supported on KubeflowDagRunner currently
if runner == 'kubeflow':
pipeline_root_param = os.path.join('gs://{{kfp-default-bucket}}',
pipeline_name, '{{workflow.uid}}')
train_data_param = data_types.RuntimeParameter(
name='train-data',
default=
'gs://renming-mlpipeline-kubeflowpipelines-default/kaggle/santander/train',
ptype=Text)
test_data_param = data_types.RuntimeParameter(
name='test-data',
default=
'gs://renming-mlpipeline-kubeflowpipelines-default/kaggle/santander/test',
ptype=Text)
pusher_target_param = data_types.RuntimeParameter(
name='pusher-destination',
default=
'gs://renming-mlpipeline-kubeflowpipelines-default/kaggle/santander/serving',
ptype=Text)
else:
pipeline_root_param = pipeline_root
train_data_param = train_data
test_data_param = test_data
pusher_target_param = pusher_target
examples = external_input(train_data_param)
example_gen = CsvExampleGen(input=examples, instance_name="train")
test_examples = external_input(test_data_param)
test_example_gen = CsvExampleGen(input=test_examples,
output_config={
'split_config': {
'splits': [{
'name': 'test',
'hash_buckets': 1
}]
}
},
instance_name="test")
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True
) # infer_feature_shape controls sparse or dense
# Transform is too slow in my side.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
trainer = Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
module_file=module_file,
train_args=trainer_pb2.TrainArgs(num_steps=train_steps),
eval_args=trainer_pb2.EvalArgs(num_steps=eval_steps),
instance_name="train",
enable_cache=False)
# 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(label_key='target')],
# tfma.SlicingSpec(feature_keys=['var_0', 'var_1']) when add more, Evaluator can't ouptput BLESSED status. It should be a bug in TFMA.
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
thresholds={
'binary_accuracy':
tfma.config.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.4}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
})
])
evaluator = 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,
instance_name="eval5")
# 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=evaluator.outputs['blessing'],
push_destination={
'filesystem': {
'base_directory': pusher_target_param
}
})
bulk_inferrer = BulkInferrer(
examples=test_example_gen.outputs['examples'],
model=trainer.outputs['model'],
# model_blessing=evaluator.outputs['blessing'],
data_spec=bulk_inferrer_pb2.DataSpec(),
model_spec=bulk_inferrer_pb2.ModelSpec(),
instance_name="bulkInferrer")
hello = component.HelloComponent(
input_data=bulk_inferrer.outputs['inference_result'],
instance_name='csvGen')
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root_param,
components=[
example_gen, statistics_gen, schema_gen, transform, trainer,
model_resolver, evaluator, pusher, hello, test_example_gen,
bulk_inferrer
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
os.path.join(pipeline_root, 'metadata.sqlite')),
beam_pipeline_args=['--direct_num_workers=0'])
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(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
# TODO(step 7): (Optional) Uncomment here to use BigQuery as a data source.
# query: Text,
preprocessing_fn: Text,
run_fn: Text,
train_args: trainer_pb2.TrainArgs,
eval_args: trainer_pb2.EvalArgs,
eval_accuracy_threshold: float,
serving_model_dir: Text,
metadata_connection_config: Optional[
metadata_store_pb2.ConnectionConfig] = None,
beam_pipeline_args: Optional[List[Text]] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
) -> pipeline.Pipeline:
"""Implements the chicago taxi pipeline with TFX."""
components = []
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=external_input(data_path))
# TODO(step 7): (Optional) Uncomment here to use BigQuery as a data source.
# example_gen = BigQueryExampleGen(query=query)
components.append(example_gen)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# TODO(step 5): Uncomment here to add StatisticsGen to the pipeline.
# components.append(statistics_gen)
# Generates schema based on statistics files.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
# TODO(step 5): Uncomment here to add SchemaGen to the pipeline.
# components.append(schema_gen)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator( # pylint: disable=unused-variable
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# TODO(step 5): Uncomment here to add ExampleValidator to the pipeline.
# components.append(example_validator)
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)
# TODO(step 6): Uncomment here to add Transform to the pipeline.
# components.append(transform)
# Uses user-provided Python function that implements a model using TF-Learn.
trainer_args = {
'run_fn':
run_fn,
'transformed_examples':
transform.outputs['transformed_examples'],
'schema':
schema_gen.outputs['schema'],
'transform_graph':
transform.outputs['transform_graph'],
'train_args':
train_args,
'eval_args':
eval_args,
'custom_executor_spec':
executor_spec.ExecutorClassSpec(trainer_executor.GenericExecutor),
}
if ai_platform_training_args is not None:
trainer_args.update({
'custom_executor_spec':
executor_spec.ExecutorClassSpec(
ai_platform_trainer_executor.GenericExecutor),
'custom_config': {
ai_platform_trainer_executor.TRAINING_ARGS_KEY:
ai_platform_training_args,
}
})
trainer = Trainer(**trainer_args)
# TODO(step 6): Uncomment here to add Trainer to the pipeline.
# components.append(trainer)
# 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))
# TODO(step 6): Uncomment here to add ResolverNode to the pipeline.
# components.append(model_resolver)
# 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(label_key='tips')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': eval_accuracy_threshold}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = 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)
# TODO(step 6): Uncomment here to add Evaluator to the pipeline.
# components.append(evaluator)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher_args = {
'model':
trainer.outputs['model'],
'model_blessing':
evaluator.outputs['blessing'],
'push_destination':
pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)),
}
if ai_platform_serving_args is not None:
pusher_args.update({
'custom_executor_spec':
executor_spec.ExecutorClassSpec(
ai_platform_pusher_executor.Executor),
'custom_config': {
ai_platform_pusher_executor.SERVING_ARGS_KEY:
ai_platform_serving_args
},
})
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
# TODO(step 6): Uncomment here to add Pusher to the pipeline.
# components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
# TODO(step 8): Change this value to control caching of execution results.
enable_cache=True,
metadata_connection_config=metadata_connection_config,
beam_pipeline_args=beam_pipeline_args,
)
def create_pipeline_components(
pipeline_root: Text,
transform_module: Text,
trainer_module: Text,
bigquery_query: Text = '',
csv_input_location: Text = '',
) -> List[base_node.BaseNode]:
"""Creates components for a simple Chicago Taxi TFX pipeline for testing.
Args:
pipeline_root: The root of the pipeline output.
transform_module: The location of the transform module file.
trainer_module: The location of the trainer module file.
bigquery_query: The query to get input data from BigQuery. If not empty,
BigQueryExampleGen will be used.
csv_input_location: The location of the input data directory.
Returns:
A list of TFX components that constitutes an end-to-end test pipeline.
"""
if bool(bigquery_query) == bool(csv_input_location):
raise ValueError(
'Exactly one example gen is expected. ',
'Please provide either bigquery_query or csv_input_location.')
if bigquery_query:
example_gen = big_query_example_gen_component.BigQueryExampleGen(
query=bigquery_query)
else:
example_gen = components.CsvExampleGen(input_base=csv_input_location)
statistics_gen = components.StatisticsGen(
examples=example_gen.outputs['examples'])
schema_gen = components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
example_validator = components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
transform = components.Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=transform_module)
latest_model_resolver = resolver.Resolver(
strategy_class=latest_artifacts_resolver.LatestArtifactsResolver,
model=channel.Channel(type=standard_artifacts.Model)).with_id(
'Resolver.latest_model_resolver')
trainer = components.Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(Executor),
transformed_examples=transform.outputs['transformed_examples'],
schema=schema_gen.outputs['schema'],
base_model=latest_model_resolver.outputs['model'],
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,
)
# Get the latest blessed model for model validation.
model_resolver = resolver.Resolver(
strategy_class=latest_blessed_model_resolver.
LatestBlessedModelResolver,
model=channel.Channel(type=standard_artifacts.Model),
model_blessing=channel.Channel(
type=standard_artifacts.ModelBlessing)).with_id(
'Resolver.latest_blessed_model_resolver')
# Set the TFMA config for Model Evaluation and Validation.
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(signature_name='eval')],
metrics_specs=[
tfma.MetricsSpec(
metrics=[tfma.MetricConfig(class_name='ExampleCount')],
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=[
tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
evaluator = components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
pusher = components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.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, schema_gen, example_validator, transform,
latest_model_resolver, trainer, model_resolver, evaluator, pusher
]
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, module_file_lite: Text,
serving_model_dir: Text, serving_model_dir_lite: Text,
metadata_path: Text,
beam_pipeline_args: List[Text]) -> pipeline.Pipeline:
"""Implements the handwritten digit classification example using TFX."""
# Brings data into the pipeline.
example_gen = ImportExampleGen(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.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
def _create_trainer(module_file, instance_name):
return Trainer(module_file=module_file,
custom_executor_spec=executor_spec.ExecutorClassSpec(
GenericExecutor),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=5000),
eval_args=trainer_pb2.EvalArgs(num_steps=100),
instance_name=instance_name)
# Uses user-provided Python function that trains a Keras model.
trainer = _create_trainer(module_file, 'mnist')
# Trains the same model as the one above, but converts it into a TFLite one.
trainer_lite = _create_trainer(module_file_lite, 'mnist_lite')
# TODO(b/150949276): Add resolver back once it supports two trainers.
# Uses TFMA to compute an evaluation statistics over features of a model and
# performs quality validation of a candidate model.
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='image_class')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.config.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.8})))
])
])
eval_config_lite = tfma.EvalConfig()
eval_config_lite.CopyFrom(eval_config)
# Informs the evaluator that the model is a TFLite model.
eval_config_lite.model_specs[0].model_type = 'tf_lite'
# Uses TFMA to compute the evaluation statistics over features of a model.
evaluator = Evaluator(examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
eval_config=eval_config,
instance_name='mnist')
# Uses TFMA to compute the evaluation statistics over features of a TFLite
# model.
evaluator_lite = Evaluator(examples=example_gen.outputs['examples'],
model=trainer_lite.outputs['model'],
eval_config=eval_config_lite,
instance_name='mnist_lite')
# 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=evaluator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir)),
instance_name='mnist')
# Checks whether the TFLite model passed the validation steps and pushes the
# model to a file destination if check passed.
pusher_lite = Pusher(model=trainer_lite.outputs['model'],
model_blessing=evaluator_lite.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=serving_model_dir_lite)),
instance_name='mnist_lite')
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[
example_gen,
statistics_gen,
schema_gen,
example_validator,
transform,
trainer,
trainer_lite,
evaluator,
evaluator_lite,
pusher,
pusher_lite,
],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
beam_pipeline_args=beam_pipeline_args)
def _create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_root: Text,
module_file: Text,
accuracy_threshold: float,
serving_model_dir: Text,
metadata_path: Text,
user_provided_schema_path: Optional[Text],
enable_tuning: bool,
enable_bulk_inferrer: bool,
examplegen_input_config: Optional[tfx.proto.Input],
examplegen_range_config: Optional[tfx.proto.RangeConfig],
resolver_range_config: Optional[tfx.proto.RangeConfig],
beam_pipeline_args: List[Text],
) -> tfx.dsl.Pipeline:
"""Implements the penguin pipeline with TFX.
Args:
pipeline_name: name of the TFX pipeline being created.
pipeline_root: root directory of the pipeline.
data_root: directory containing the penguin data.
module_file: path to files used in Trainer and Transform components.
accuracy_threshold: minimum accuracy to push the model.
serving_model_dir: filepath to write pipeline SavedModel to.
metadata_path: path to local pipeline ML Metadata store.
user_provided_schema_path: path to user provided schema file.
enable_tuning: If True, the hyperparameter tuning through KerasTuner is
enabled.
enable_bulk_inferrer: If True, the generated model will be used for a
batch inference.
examplegen_input_config: ExampleGen's input_config.
examplegen_range_config: ExampleGen's range_config.
resolver_range_config: SpansResolver's range_config. Specify this will
enable SpansResolver to get a window of ExampleGen's output Spans for
transform and training.
beam_pipeline_args: list of beam pipeline options for LocalDAGRunner. Please
refer to https://beam.apache.org/documentation/runners/direct/.
Returns:
A TFX pipeline object.
"""
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = tfx.components.CsvExampleGen(
input_base=os.path.join(data_root, 'labelled'),
input_config=examplegen_input_config,
range_config=examplegen_range_config)
# Computes statistics over data for visualization and example validation.
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
if user_provided_schema_path:
# Import user-provided schema.
schema_importer = tfx.dsl.Importer(
source_uri=user_provided_schema_path,
artifact_type=tfx.types.standard_artifacts.Schema).with_id(
'schema_importer')
schema = schema_importer.outputs['result']
else:
# Generates schema based on statistics files.
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=True)
schema = schema_gen.outputs['schema']
# Performs anomaly detection based on statistics and data schema.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'], schema=schema)
# Gets multiple Spans for transform and training.
if resolver_range_config:
examples_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.SpanRangeStrategy,
config={
'range_config': resolver_range_config
},
examples=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.Examples,
producer_component_id=example_gen.id)).with_id('span_resolver')
# Performs transformations and feature engineering in training and serving.
transform = tfx.components.Transform(
examples=(examples_resolver.outputs['examples'] if
resolver_range_config else example_gen.outputs['examples']),
schema=schema,
module_file=module_file)
# Tunes the hyperparameters for model training based on user-provided Python
# function. Note that once the hyperparameters are tuned, you can drop the
# Tuner component from pipeline and feed Trainer with tuned hyperparameters.
if enable_tuning:
tuner = tfx.components.Tuner(
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
train_args=tfx.proto.TrainArgs(num_steps=20),
eval_args=tfx.proto.EvalArgs(num_steps=5))
# Uses user-provided Python function that trains a model.
trainer = tfx.components.Trainer(
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema,
# If Tuner is in the pipeline, Trainer can take Tuner's output
# best_hyperparameters artifact as input and utilize it in the user module
# code.
#
# If there isn't Tuner in the pipeline, either use ImporterNode to import
# a previous Tuner's output to feed to Trainer, or directly use the tuned
# hyperparameters in user module code and set hyperparameters to None
# here.
#
# Example of ImporterNode,
# hparams_importer = ImporterNode(
# source_uri='path/to/best_hyperparameters.txt',
# artifact_type=HyperParameters).with_id('import_hparams')
# ...
# hyperparameters = hparams_importer.outputs['result'],
hyperparameters=(tuner.outputs['best_hyperparameters']
if enable_tuning else None),
train_args=tfx.proto.TrainArgs(num_steps=100),
eval_args=tfx.proto.EvalArgs(num_steps=5))
# Get the latest blessed model for model validation.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
# Uses TFMA to compute evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': accuracy_threshold}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir)))
# Showcase for BulkInferrer component.
if enable_bulk_inferrer:
# Generates unlabelled examples.
example_gen_unlabelled = tfx.components.CsvExampleGen(
input_base=os.path.join(data_root, 'unlabelled')).with_id(
'CsvExampleGen_Unlabelled')
# Performs offline batch inference.
bulk_inferrer = tfx.components.BulkInferrer(
examples=example_gen_unlabelled.outputs['examples'],
model=trainer.outputs['model'],
# Empty data_spec.example_splits will result in using all splits.
data_spec=tfx.proto.DataSpec(),
model_spec=tfx.proto.ModelSpec())
components_list = [
example_gen,
statistics_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
pusher,
]
if user_provided_schema_path:
components_list.append(schema_importer)
else:
components_list.append(schema_gen)
if resolver_range_config:
components_list.append(examples_resolver)
if enable_tuning:
components_list.append(tuner)
if enable_bulk_inferrer:
components_list.append(example_gen_unlabelled)
components_list.append(bulk_inferrer)
return tfx.dsl.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components_list,
enable_cache=True,
metadata_connection_config=tfx.orchestration.metadata.
sqlite_metadata_connection_config(metadata_path),
beam_pipeline_args=beam_pipeline_args)
