def test_batch_model_validate(self):
input_example_meta = af.register_example(name='batch_train_example',
support_type=ExampleSupportType.EXAMPLE_BOTH)
model_meta = af.register_model(model_name='mnist_model',
model_type=ModelType.SAVED_MODEL)
with af.config(af.BaseJobConfig(platform='local', engine='python', job_name='evaluate')):
input_example = af.read_example(example_info=input_example_meta,
executor=PythonObjectExecutor(python_object=ReadBatchExample()))
batch_train = af.train(input_data_list=[input_example],
executor=PythonObjectExecutor(python_object=TrainBatchMnistModel()),
model_info=model_meta)
model_validate = af.model_validate(input_data_list=[input_example],
model_info=model_meta,
executor=PythonObjectExecutor(python_object=BatchModelValidate()),
output_num=0)
af.stop_before_control_dependency(model_validate, batch_train)
workflow_id = af.run(test_util.get_project_path())
res = af.wait_workflow_execution_finished(workflow_id)
self.assertEqual(0, res)
def run_project(project_root_path):
af.set_project_config_file(project_root_path + "/project.yaml")
project_name = af.project_config().get_project_name()
artifact_prefix = project_name + "."
validate_trigger = af.external_trigger(name='validate')
push_trigger = af.external_trigger(name='push')
with af.global_config_file(project_root_path + '/resources/workflow_config.yaml'):
with af.config('train_job'):
train_example = af.register_example(name=artifact_prefix + 'train_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=EXAMPLE_URI.format('train'))
train_read_example = af.read_example(example_info=train_example,
executor=PythonObjectExecutor(python_object=TrainExampleReader()))
train_transform = af.transform(input_data_list=[train_read_example],
executor=PythonObjectExecutor(python_object=TrainExampleTransformer()))
train_model = af.register_model(model_name=artifact_prefix + 'logistic-regression',
model_type=ModelType.SAVED_MODEL,
model_desc='logistic regression model')
train_channel = af.train(input_data_list=[train_transform],
executor=PythonObjectExecutor(python_object=ModelTrainer()),
model_info=train_model)
with af.config('validate_job'):
validate_example = af.register_example(name=artifact_prefix + 'validate_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=EXAMPLE_URI.format('evaluate'),
data_format='npz')
validate_read_example = af.read_example(example_info=validate_example,
executor=PythonObjectExecutor(
python_object=ValidateExampleReader()))
validate_transform = af.transform(input_data_list=[validate_read_example],
executor=PythonObjectExecutor(python_object=ValidateTransformer()))
validate_artifact_name = artifact_prefix + 'validate_artifact'
validate_artifact = af.register_artifact(name=validate_artifact_name,
stream_uri=get_file_dir(__file__) + '/validate_result')
validate_channel = af.model_validate(input_data_list=[validate_transform],
model_info=train_model,
executor=PythonObjectExecutor(
python_object=ModelValidator(validate_artifact_name)),
)
with af.config('push_job'):
# Push model to serving
# Register metadata of pushed model
push_model_artifact_name = artifact_prefix + 'push_model_artifact'
push_model_artifact = af.register_artifact(name=push_model_artifact_name,
stream_uri=get_file_dir(__file__) + '/pushed_model')
push_channel = af.push_model(model_info=train_model,
executor=PythonObjectExecutor(
python_object=ModelPusher(push_model_artifact_name)))
with af.config('predict_job'):
predict_example = af.register_example(name=artifact_prefix + 'predict_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=EXAMPLE_URI.format('predict'))
predict_read_example = af.read_example(example_info=predict_example,
executor=PythonObjectExecutor(python_object=PredictExampleReader()))
predict_transform = af.transform(input_data_list=[predict_read_example],
executor=PythonObjectExecutor(python_object=PredictTransformer()))
predict_channel = af.predict(input_data_list=[predict_transform],
model_info=train_model,
executor=PythonObjectExecutor(python_object=ModelPredictor()))
write_example = af.register_example(name=artifact_prefix + 'export_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=get_file_dir(__file__) + '/predict_result')
af.write_example(input_data=predict_channel,
example_info=write_example,
executor=PythonObjectExecutor(python_object=ExampleWriter()))
af.model_version_control_dependency(src=validate_channel,
model_version_event_type=ModelVersionEventType.MODEL_GENERATED,
dependency=validate_trigger, model_name=train_model.name)
af.model_version_control_dependency(src=push_channel,
model_version_event_type=ModelVersionEventType.MODEL_VALIDATED,
dependency=push_trigger, model_name=train_model.name)
# Run workflow
transform_dag = project_name
af.deploy_to_airflow(project_root_path, dag_id=transform_dag)
af.run(project_path=project_root_path,
dag_id=transform_dag,
scheduler_type=SchedulerType.AIRFLOW)
def run_workflow():
# Init project
af.init_ai_flow_context()
artifact_prefix = af.current_project_config().get_project_name() + "."
# Training of model
with af.job_config('train'):
# Register metadata of training data(dataset) and read dataset(i.e. training dataset)
train_dataset = af.register_dataset(name=artifact_prefix + 'train_dataset',
uri=DATASET_URI.format('train'))
train_read_dataset = af.read_dataset(dataset_info=train_dataset,
read_dataset_processor=DatasetReader())
# Register model metadata and train model
train_model = af.register_model(model_name=artifact_prefix + 'KNN',
model_desc='KNN model')
train_channel = af.train(input=[train_read_dataset],
training_processor=ModelTrainer(),
model_info=train_model)
# Validation of model
with af.job_config('validate'):
# Read validation dataset
validate_dataset = af.register_dataset(name=artifact_prefix + 'validate_dataset',
uri=DATASET_URI.format('test'))
# Validate model before it is used to predict
validate_read_dataset = af.read_dataset(dataset_info=validate_dataset,
read_dataset_processor=ValidateDatasetReader())
validate_artifact_name = artifact_prefix + 'validate_artifact'
validate_artifact = af.register_artifact(name=validate_artifact_name,
uri=get_file_dir(__file__) + '/validate_result')
validate_channel = af.model_validate(input=[validate_read_dataset],
model_info=train_model,
model_validation_processor=ModelValidator(validate_artifact_name))
# Prediction(Inference) using flink
with af.job_config('predict'):
# Read test data and do prediction
predict_dataset = af.register_dataset(name=artifact_prefix + 'predict_dataset',
uri=DATASET_URI.format('test'))
predict_read_dataset = af.read_dataset(dataset_info=predict_dataset,
read_dataset_processor=Source())
predict_channel = af.predict(input=[predict_read_dataset],
model_info=train_model,
prediction_processor=Predictor())
# Save prediction result
write_dataset = af.register_dataset(name=artifact_prefix + 'write_dataset',
uri=get_file_dir(__file__) + '/predict_result.csv')
af.write_dataset(input=predict_channel,
dataset_info=write_dataset,
write_dataset_processor=Sink())
# Define relation graph connected by control edge: train -> validate -> predict
af.action_on_model_version_event(job_name='validate',
model_version_event_type=ModelVersionEventType.MODEL_GENERATED,
model_name=train_model.name)
af.action_on_model_version_event(job_name='predict',
model_version_event_type=ModelVersionEventType.MODEL_VALIDATED,
model_name=train_model.name)
# Submit workflow
af.workflow_operation.submit_workflow(af.current_workflow_config().workflow_name)
# Run workflow
af.workflow_operation.start_new_workflow_execution(af.current_workflow_config().workflow_name)
def run_project(project_root_path):
af.set_project_config_file(project_root_path + "/project.yaml")
project_name = af.project_config().get_project_name()
artifact_prefix = project_name + "."
validate_trigger = af.external_trigger(name='validate')
push_trigger = af.external_trigger(name='push')
with af.global_config_file(project_root_path + '/resources/workflow_config.yaml'):
# the config of train job is a periodic job which means it will
# run every `interval`(defined in workflow_config.yaml) seconds
with af.config('train_job'):
# Register metadata raw training data(example) and read example(i.e. training dataset)
train_example = af.register_example(name=artifact_prefix + 'train_example',
support_type=ExampleSupportType.EXAMPLE_BATCH,
batch_uri=EXAMPLE_URI.format('train'))
train_read_example = af.read_example(example_info=train_example,
executor=PythonObjectExecutor(python_object=ExampleReader()))
# Transform(preprocessing) example
train_transform = af.transform(input_data_list=[train_read_example],
executor=PythonObjectExecutor(python_object=ExampleTransformer()))
# Register model metadata and train model
train_model = af.register_model(model_name=artifact_prefix + 'logistic-regression',
model_type=ModelType.SAVED_MODEL,
model_desc='logistic regression model')
train_channel = af.train(input_data_list=[train_transform],
executor=PythonObjectExecutor(python_object=ModelTrainer()),
model_info=train_model)
with af.config('validate_job'):
# Validation of model
# Read validation dataset and validate model before it is used to predict
validate_example = af.register_example(name=artifact_prefix + 'validate_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
batch_uri=EXAMPLE_URI.format('evaluate'))
validate_read_example = af.read_example(example_info=validate_example,
executor=PythonObjectExecutor(
python_object=ValidateExampleReader()))
validate_transform = af.transform(input_data_list=[validate_read_example],
executor=PythonObjectExecutor(python_object=ValidateTransformer()))
validate_artifact_name = artifact_prefix + 'validate_artifact'
validate_artifact = af.register_artifact(name=validate_artifact_name,
batch_uri=get_file_dir(__file__) + '/validate_result')
validate_channel = af.model_validate(input_data_list=[validate_transform],
model_info=train_model,
executor=PythonObjectExecutor(
python_object=ModelValidator(validate_artifact_name)))
with af.config('push_job'):
# Push model to serving
# Register metadata of pushed model
push_model_artifact_name = artifact_prefix + 'push_model_artifact'
push_model_artifact = af.register_artifact(name=push_model_artifact_name,
batch_uri=get_file_dir(__file__) + '/pushed_model')
push_channel = af.push_model(model_info=train_model,
executor=PythonObjectExecutor(
python_object=ModelPusher(push_model_artifact_name)))
with af.config('predict_job'):
# Prediction(Inference)
predict_example = af.register_example(name=artifact_prefix + 'predict_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=EXAMPLE_URI.format('predict'))
predict_read_example = af.read_example(example_info=predict_example,
executor=PythonObjectExecutor(python_object=PredictExampleReader()))
predict_transform = af.transform(input_data_list=[predict_read_example],
executor=PythonObjectExecutor(python_object=PredictTransformer()))
predict_channel = af.predict(input_data_list=[predict_transform],
model_info=train_model,
executor=PythonObjectExecutor(python_object=ModelPredictor()))
# Save prediction result
write_example = af.register_example(name=artifact_prefix + 'write_example',
support_type=ExampleSupportType.EXAMPLE_STREAM,
stream_uri=get_file_dir(__file__) + '/predict_result')
af.write_example(input_data=predict_channel,
example_info=write_example,
executor=PythonObjectExecutor(python_object=ExampleWriter()))
# Define relation graph connected by control edge:
# Once a round of training is done, validator will be launched and
# pusher will be launched if the new model is better.
# Prediction will start once the first round of training is done and
# when pusher pushes(deploys) a new model, the predictor will use the latest deployed model as well.
af.model_version_control_dependency(src=validate_channel,
model_version_event_type=ModelVersionEventType.MODEL_GENERATED,
dependency=validate_trigger, model_name=train_model.name)
af.model_version_control_dependency(src=push_channel,
model_version_event_type=ModelVersionEventType.MODEL_VALIDATED,
dependency=push_trigger, model_name=train_model.name)
# Run workflow
transform_dag = project_name
af.deploy_to_airflow(project_root_path, dag_id=transform_dag)
af.run(project_path=project_root_path,
dag_id=transform_dag,
scheduler_type=SchedulerType.AIRFLOW)
def run_workflow():
af.init_ai_flow_context()
artifact_prefix = af.current_project_config().get_project_name() + "."
# the config of train job is a periodic job which means it will
# run every `interval`(defined in workflow_config.yaml) seconds
with af.job_config('train'):
# Register metadata raw training data(dataset) and read dataset(i.e. training dataset)
train_dataset = af.register_dataset(name=artifact_prefix +
'train_dataset',
uri=DATASET_URI.format('train'))
train_read_dataset = af.read_dataset(
dataset_info=train_dataset, read_dataset_processor=DatasetReader())
# Transform(preprocessing) dataset
train_transform = af.transform(
input=[train_read_dataset],
transform_processor=DatasetTransformer())
# Register model metadata and train model
train_model = af.register_model(model_name=artifact_prefix +
'logistic-regression',
model_desc='logistic regression model')
train_channel = af.train(input=[train_transform],
training_processor=ModelTrainer(),
model_info=train_model)
with af.job_config('validate'):
# Validation of model
# Read validation dataset and validate model before it is used to predict
validate_dataset = af.register_dataset(
name=artifact_prefix + 'validate_dataset',
uri=DATASET_URI.format('evaluate'))
validate_read_dataset = af.read_dataset(
dataset_info=validate_dataset,
read_dataset_processor=ValidateDatasetReader())
validate_transform = af.transform(
input=[validate_read_dataset],
transform_processor=ValidateTransformer())
validate_artifact_name = artifact_prefix + 'validate_artifact'
validate_artifact = af.register_artifact(name=validate_artifact_name,
uri=get_file_dir(__file__) +
'/validate_result')
validate_channel = af.model_validate(
input=[validate_transform],
model_info=train_model,
model_validation_processor=ModelValidator(validate_artifact_name))
with af.job_config('push'):
# Push model to serving
# Register metadata of pushed model
push_model_artifact_name = artifact_prefix + 'push_model_artifact'
push_model_artifact = af.register_artifact(
name=push_model_artifact_name,
uri=get_file_dir(__file__) + '/pushed_model')
af.push_model(
model_info=train_model,
pushing_model_processor=ModelPusher(push_model_artifact_name))
with af.job_config('predict'):
# Prediction(Inference)
predict_dataset = af.register_dataset(
name=artifact_prefix + 'predict_dataset',
uri=DATASET_URI.format('predict'))
predict_read_dataset = af.read_dataset(
dataset_info=predict_dataset,
read_dataset_processor=PredictDatasetReader())
predict_transform = af.transform(
input=[predict_read_dataset],
transform_processor=PredictTransformer())
predict_channel = af.predict(input=[predict_transform],
model_info=train_model,
prediction_processor=ModelPredictor())
# Save prediction result
write_dataset = af.register_dataset(
name=artifact_prefix + 'write_dataset',
uri=get_file_dir(__file__) + '/predict_result')
af.write_dataset(input=predict_channel,
dataset_info=write_dataset,
write_dataset_processor=DatasetWriter())
# Define relation graph connected by control edge:
# Once a round of training is done, validator will be launched and
# pusher will be launched if the new model is better.
# Prediction will start once the first round of training is done and
# when pusher pushes(deploys) a new model, the predictor will use the latest deployed model as well.
af.action_on_model_version_event(
job_name='validate',
model_version_event_type=ModelVersionEventType.MODEL_GENERATED,
model_name=train_model.name)
af.action_on_model_version_event(
job_name='push',
model_version_event_type=ModelVersionEventType.MODEL_VALIDATED,
model_name=train_model.name)
# Run workflow
af.workflow_operation.submit_workflow(
af.current_workflow_config().workflow_name)
af.workflow_operation.start_new_workflow_execution(
af.current_workflow_config().workflow_name)
def run_workflow():
af.init_ai_flow_context()
artifact_prefix = af.current_project_config().get_project_name() + "."
with af.job_config('train'):
# Register metadata raw training data(dataset) and read dataset(i.e. training dataset)
train_dataset = af.register_dataset(name=artifact_prefix +
'train_dataset',
uri=DATASET_URI.format('train'))
train_read_dataset = af.read_dataset(
dataset_info=train_dataset,
read_dataset_processor=TrainDatasetReader())
train_transform = af.transform(
input=[train_read_dataset],
transform_processor=TrainDatasetTransformer())
train_model = af.register_model(model_name=artifact_prefix +
'logistic-regression',
model_desc='logistic regression model')
train_channel = af.train(input=[train_transform],
training_processor=ModelTrainer(),
model_info=train_model)
with af.job_config('validate'):
validate_dataset = af.register_dataset(
name=artifact_prefix + 'validate_dataset',
uri=DATASET_URI.format('evaluate'))
validate_read_dataset = af.read_dataset(
dataset_info=validate_dataset,
read_dataset_processor=ValidateDatasetReader())
validate_transform = af.transform(
input=[validate_read_dataset],
transform_processor=ValidateTransformer())
validate_artifact_name = artifact_prefix + 'validate_artifact'
validate_artifact = af.register_artifact(name=validate_artifact_name,
uri=get_file_dir(__file__) +
'/validate_result')
validate_channel = af.model_validate(
input=[validate_transform],
model_info=train_model,
model_validation_processor=ModelValidator(validate_artifact_name))
with af.job_config('push'):
# Push model to serving
# Register metadata of pushed model
push_model_artifact_name = artifact_prefix + 'push_model_artifact'
push_model_artifact = af.register_artifact(
name=push_model_artifact_name,
uri=get_file_dir(__file__) + '/pushed_model')
af.push_model(
model_info=train_model,
pushing_model_processor=ModelPusher(push_model_artifact_name))
with af.job_config('predict'):
predict_dataset = af.register_dataset(
name=artifact_prefix + 'predict_dataset',
uri=DATASET_URI.format('predict'))
predict_read_dataset = af.read_dataset(
dataset_info=predict_dataset,
read_dataset_processor=PredictDatasetReader())
predict_transform = af.transform(
input=[predict_read_dataset],
transform_processor=PredictTransformer())
predict_channel = af.predict(input=[predict_transform],
model_info=train_model,
prediction_processor=ModelPredictor())
write_dataset = af.register_dataset(
name=artifact_prefix + 'export_dataset',
uri=get_file_dir(__file__) + '/predict_result')
af.write_dataset(input=predict_channel,
dataset_info=write_dataset,
write_dataset_processor=DatasetWriter())
af.action_on_model_version_event(
job_name='validate',
model_version_event_type=ModelVersionEventType.MODEL_GENERATED,
model_name=train_model.name)
af.action_on_model_version_event(
job_name='push',
model_version_event_type=ModelVersionEventType.MODEL_VALIDATED,
model_name=train_model.name)
# Run workflow
af.workflow_operation.submit_workflow(
af.current_workflow_config().workflow_name)
af.workflow_operation.start_new_workflow_execution(
af.current_workflow_config().workflow_name)
def run_workflow():
af.init_ai_flow_context()
artifact_prefix = af.current_project_config().get_project_name() + "."
with af.job_config('train'):
# Training of model
# Register metadata raw training data(dataset) and read dataset(i.e. training dataset)
train_dataset = af.register_dataset(name=artifact_prefix +
'train_dataset',
uri=DATASET_URI.format('train'))
train_read_dataset = af.read_dataset(
dataset_info=train_dataset, read_dataset_processor=DatasetReader())
# Transform(preprocessing) dataset
train_transform = af.transform(
input=[train_read_dataset],
transform_processor=DatasetTransformer())
# Register model metadata and train model
train_model = af.register_model(model_name=artifact_prefix +
'logistic-regression',
model_desc='logistic regression model')
train_channel = af.train(input=[train_transform],
training_processor=ModelTrainer(),
model_info=train_model)
with af.job_config('evaluate'):
# Evaluation of model
evaluate_dataset = af.register_dataset(
name=artifact_prefix + 'evaluate_dataset',
uri=DATASET_URI.format('evaluate'))
evaluate_read_dataset = af.read_dataset(
dataset_info=evaluate_dataset,
read_dataset_processor=EvaluateDatasetReader())
evaluate_transform = af.transform(
input=[evaluate_read_dataset],
transform_processor=EvaluateTransformer())
# Register disk path used to save evaluate result
evaluate_artifact_name = artifact_prefix + 'evaluate_artifact'
evaluate_artifact = af.register_artifact(name=evaluate_artifact_name,
uri=get_file_dir(__file__) +
'/evaluate_result')
# Evaluate model
evaluate_channel = af.evaluate(
input=[evaluate_transform],
model_info=train_model,
evaluation_processor=ModelEvaluator(evaluate_artifact_name))
with af.job_config('validate'):
# Validation of model
# Read validation dataset and validate model before it is used to predict
validate_dataset = af.register_dataset(
name=artifact_prefix + 'validate_dataset',
uri=DATASET_URI.format('evaluate'))
validate_read_dataset = af.read_dataset(
dataset_info=validate_dataset,
read_dataset_processor=ValidateDatasetReader())
validate_transform = af.transform(
input=[validate_read_dataset],
transform_processor=ValidateTransformer())
validate_artifact_name = artifact_prefix + 'validate_artifact'
validate_artifact = af.register_artifact(name=validate_artifact_name,
uri=get_file_dir(__file__) +
'/validate_result')
validate_channel = af.model_validate(
input=[validate_transform],
model_info=train_model,
model_validation_processor=ModelValidator(validate_artifact_name))
with af.job_config('push'):
# Push model to serving
# Register metadata of pushed model
push_model_artifact_name = artifact_prefix + 'push_model_artifact'
push_model_artifact = af.register_artifact(
name=push_model_artifact_name,
uri=get_file_dir(__file__) + '/pushed_model')
af.push_model(
model_info=train_model,
pushing_model_processor=ModelPusher(push_model_artifact_name))
with af.job_config('predict'):
# Prediction(Inference)
predict_dataset = af.register_dataset(
name=artifact_prefix + 'predict_dataset',
uri=DATASET_URI.format('predict'))
predict_read_dataset = af.read_dataset(
dataset_info=predict_dataset,
read_dataset_processor=PredictDatasetReader())
predict_transform = af.transform(
input=[predict_read_dataset],
transform_processor=PredictTransformer())
predict_channel = af.predict(input=[predict_transform],
model_info=train_model,
prediction_processor=ModelPredictor())
# Save prediction result
write_dataset = af.register_dataset(
name=artifact_prefix + 'write_dataset',
uri=get_file_dir(__file__) + '/predict_result')
af.write_dataset(input=predict_channel,
dataset_info=write_dataset,
write_dataset_processor=DatasetWriter())
# Define relation graph connected by control edge: train -> evaluate -> validate -> push -> predict
af.action_on_job_status('evaluate', 'train')
af.action_on_job_status('validate', 'evaluate')
af.action_on_job_status('push', 'validate')
af.action_on_job_status('predict', 'push')
# Run workflow
af.workflow_operation.submit_workflow(
af.current_workflow_config().workflow_name)
af.workflow_operation.start_new_workflow_execution(
af.current_workflow_config().workflow_name)
