overwrite={'has_diabetes': {
'transform': [{'method': 'no_transform', 'parameters': {}}]}}
))
# Add a trainer
training_pipeline.add_trainer(FeedForwardTrainer(
loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epochs=20))
# Add an evaluator
training_pipeline.add_evaluator(
TFMAEvaluator(slices=[['has_diabetes']],
metrics={'has_diabetes': ['binary_crossentropy',
'binary_accuracy']}))
# Run the pipeline on a Google Cloud VM and train on GCP as well
# In order for this to work, the orchestrator and the backend should be in the
# same GCP project. Also, the metadata store and artifact store should be
# accessible by the orchestrator VM and the GCAIP worker VM.
# Note: If you are using a custom Trainer, then you need
# to build a new Docker image based on the ZenML Trainer image, and pass that
# into the `image` parameter in the SingleGPUTrainingGCAIPBackend.
# Define the orchestrator backend
orchestrator_backend = OrchestratorGCPBackend(
cloudsql_connection_name=cloudsql_connection_name,
def get_tfx_component_list(self, config: Dict[Text, Any]) -> List:
"""
Builds the training pipeline as a series of TFX components.
Args:
config: A ZenML configuration in dictionary format.
Returns:
A chronological list of TFX components making up the training
pipeline.
"""
steps = config[keys.GlobalKeys.STEPS]
component_list = []
############
# RAW DATA #
############
data_config = steps[keys.TrainingSteps.DATA]
data = DataGen(source=data_config[keys.StepKeys.SOURCE],
source_args=data_config[keys.StepKeys.ARGS]).with_id(
GDPComponent.DataGen.name)
statistics_data = StatisticsGen(
examples=data.outputs.examples).with_id(
GDPComponent.DataStatistics.name)
schema_data = SchemaGen(
statistics=statistics_data.outputs.output, ).with_id(
GDPComponent.DataSchema.name)
component_list.extend([data, statistics_data, schema_data])
datapoints = data.outputs.examples
#############
# SPLITTING #
#############
# Block to read the data from the corresponding BQ table
split_config = steps[keys.TrainingSteps.SPLIT]
splits = SplitGen(
input_examples=datapoints,
source=split_config[keys.StepKeys.SOURCE],
source_args=split_config[keys.StepKeys.ARGS],
schema=schema_data.outputs.schema,
statistics=statistics_data.outputs.output,
).with_id(GDPComponent.SplitGen.name)
datapoints = splits.outputs.examples
statistics_split = StatisticsGen(examples=datapoints).with_id(
GDPComponent.SplitStatistics.name)
schema_split = SchemaGen(
statistics=statistics_split.outputs.output, ).with_id(
GDPComponent.SplitSchema.name)
schema = schema_split.outputs.schema
component_list.extend([splits, statistics_split, schema_split])
##############
# SEQUENCING #
##############
if keys.TrainingSteps.SEQUENCER in steps:
sequencer_config = steps[keys.TrainingSteps.SEQUENCER]
sequencer = Sequencer(
input_examples=datapoints,
schema=schema,
statistics=statistics_split.outputs.statistics,
source=sequencer_config[keys.StepKeys.SOURCE],
source_args=sequencer_config[keys.StepKeys.ARGS]).with_id(
GDPComponent.Sequencer.name)
sequencer_statistics = StatisticsGen(
examples=sequencer.outputs.output_examples).with_id(
GDPComponent.SequencerStatistics.name)
sequencer_schema = SchemaGen(
statistics=sequencer_statistics.outputs.output,
infer_feature_shape=True,
).with_id(GDPComponent.SequencerSchema.name)
datapoints = sequencer.outputs.output_examples
schema = sequencer_schema.outputs.schema
component_list.extend(
[sequencer, sequencer_statistics, sequencer_schema])
#################
# PREPROCESSING #
#################
transform = Transform(
preprocessing_fn=constants.PREPROCESSING_FN,
examples=datapoints,
schema=schema,
custom_config=steps[keys.TrainingSteps.PREPROCESSER]).with_id(
GDPComponent.Transform.name)
component_list.extend([transform])
############
# TRAINING #
############
training_backend: TrainingLocalBackend = \
self.backends_dict[TrainingLocalBackend.BACKEND_KEY]
training_kwargs = {
'custom_executor_spec': training_backend.get_executor_spec(),
'custom_config': steps[keys.TrainingSteps.TRAINER]
}
training_kwargs['custom_config'].update(
training_backend.get_custom_config())
trainer = Trainer(
transformed_examples=transform.outputs.transformed_examples,
transform_graph=transform.outputs.transform_graph,
run_fn=constants.TRAINER_FN,
schema=schema,
train_args=trainer_pb2.TrainArgs(),
eval_args=trainer_pb2.EvalArgs(),
**training_kwargs).with_id(GDPComponent.Trainer.name)
component_list.extend([trainer])
#############
# EVALUATOR #
#############
if keys.TrainingSteps.EVALUATOR in steps:
from zenml.utils import source_utils
eval_module = '.'.join(
constants.EVALUATOR_MODULE_FN.split('.')[:-1])
eval_module_file = constants.EVALUATOR_MODULE_FN.split('.')[-1]
abs_path = source_utils.get_absolute_path_from_module(eval_module)
custom_extractor_path = os.path.join(abs_path,
eval_module_file) + '.py'
eval_step: TFMAEvaluator = TFMAEvaluator.from_config(
steps[keys.TrainingSteps.EVALUATOR])
eval_config = eval_step.build_eval_config()
evaluator = Evaluator(
examples=transform.outputs.transformed_examples,
model=trainer.outputs.model,
eval_config=eval_config,
module_file=custom_extractor_path,
).with_id(GDPComponent.Evaluator.name)
component_list.append(evaluator)
###########
# SERVING #
###########
if keys.TrainingSteps.DEPLOYER in steps:
serving_args = steps[keys.TrainingSteps.DEPLOYER]['args']
project_id = serving_args['project_id']
output_base_dir = self.artifact_store.path
if 'model_name' in serving_args:
model_name = serving_args['model_name']
else:
model_name = self.pipeline_name().replace('-', '_')
gcaip_deployer = GCAIPDeployer(output_base_dir=output_base_dir,
project_id=project_id,
model_name=model_name)
pusher_config = gcaip_deployer.build_pusher_config()
pusher_executor_spec = gcaip_deployer.get_executor_spec()
pusher = Pusher(model_export=trainer.outputs.output,
custom_executor_spec=pusher_executor_spec,
**pusher_config).with_id(
GDPComponent.Deployer.name)
component_list.append(pusher)
return component_list
features=['times_pregnant', 'pgc', 'dbp', 'tst', 'insulin', 'bmi',
'pedigree', 'age'],
labels=['has_diabetes'],
overwrite={'has_diabetes': {
'transform': [{'method': 'no_transform', 'parameters': {}}]}}
).with_backend(processing_backend)
)
# Add a trainer
training_pipeline.add_trainer(FeedForwardTrainer(
loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epochs=20))
# Add an evaluator
training_pipeline.add_evaluator(
TFMAEvaluator(
slices=[['has_diabetes']],
metrics={'has_diabetes': ['binary_crossentropy', 'binary_accuracy']}
).with_backend(processing_backend)
)
# Define the artifact store
artifact_store = ArtifactStore(
os.path.join(GCP_BUCKET, 'dataflow_processing/artifact_store'))
# Run the pipeline
training_pipeline.run(artifact_store=artifact_store)