def test_three_task(self): with af.job_config('task_1'): af.user_define_operation(processor=None) with af.job_config('task_2'): af.user_define_operation(processor=None) with af.job_config('task_3'): af.user_define_operation(processor=None) af.action_on_event(job_name='task_3', event_key='a', event_type='a', event_value='a', sender='task_1') af.action_on_job_status(job_name='task_3', upstream_job_name='task_2', upstream_job_status=Status.FINISHED, action=JobAction.START) w = af.workflow_operation.submit_workflow( workflow_name='test_dag_generator') code = w.properties.get('code') self.assertTrue( ".subscribe_event('a', 'a', 'default', 'task_1')" in code) # Now do not support the event_type equals JOB_STATUS_CHANGED event. # self.assertTrue(".subscribe_event('test_dag_generator', 'JOB_STATUS_CHANGED', 'test_project', 'task_2')" in code) self.assertTrue( ".set_events_handler(AIFlowHandler(configs_op_" in code)
def main(): af.init_ai_flow_context() with af.job_config('task_1'): af.user_define_operation(BashProcessor("echo hello")) with af.job_config('task_2'): af.user_define_operation(BashProcessor("echo hello")) af.action_on_job_status('task_2', 'task_1') workflow_name = af.current_workflow_config().workflow_name stop_workflow_executions(workflow_name) af.workflow_operation.submit_workflow(workflow_name) af.workflow_operation.start_new_workflow_execution(workflow_name)
def main(): af.init_ai_flow_context() with af.job_config('task_1'): af.user_define_operation(BashProcessor("sleep 30")) with af.job_config('task_2'): af.user_define_operation(BashProcessor("sleep 60")) with af.job_config('task_3'): af.user_define_operation(BashProcessor("echo hello")) af.action_on_job_status('task_2', 'task_1', upstream_job_status=Status.RUNNING, action=JobAction.START) af.action_on_job_status('task_2', 'task_1', upstream_job_status=Status.FINISHED, action=JobAction.STOP) af.action_on_job_status('task_3', 'task_1', upstream_job_status=Status.RUNNING, action=JobAction.START) af.action_on_job_status('task_3', 'task_2', upstream_job_status=Status.KILLED, action=JobAction.RESTART) workflow_name = af.current_workflow_config().workflow_name stop_workflow_executions(workflow_name) af.workflow_operation.submit_workflow(workflow_name) af.workflow_operation.start_new_workflow_execution(workflow_name)
def test_action_on_job_status_two_status(self): with af.job_config('task_1'): af.user_define_operation(processor=None) with af.job_config('task_2'): af.user_define_operation(processor=None) af.action_on_job_status(job_name='task_2', upstream_job_name='task_1', upstream_job_status=Status.RUNNING, action=JobAction.START) af.action_on_job_status(job_name='task_2', upstream_job_name='task_1', upstream_job_status=Status.FINISHED, action=JobAction.STOP) w = af.workflow_operation.submit_workflow( workflow_name='test_dag_generator') code = w.properties.get('code') self.assertTrue('"event_value": "RUNNING"' in code) self.assertTrue('"event_value": "FINISHED"' in code)
def run_workflow(client: NotificationClient): with af.job_config('task_1'): af.user_define_operation(processor=bash.BashProcessor(bash_command='echo "Xiao ming hello world!"')) with af.job_config('task_2'): af.user_define_operation(processor=bash.BashProcessor(bash_command='echo "Xiao li hello world!"')) af.action_on_job_status('task_2', 'task_1', Status.FINISHED, JobAction.START) workflow_info = af.workflow_operation.submit_workflow( workflow_name=af.current_workflow_config().workflow_name) workflow_execution = af.workflow_operation.start_new_workflow_execution( workflow_name=af.current_workflow_config().workflow_name) while True: with create_session() as session: ti = session.query(TaskInstance)\ .filter(TaskInstance.dag_id == 'test_project.{}'.format(af.current_workflow_config().workflow_name), TaskInstance.task_id == 'task_2')\ .first() if ti is not None and ti.state == State.SUCCESS: break else: time.sleep(1)
def test_action_on_job_status(self): with af.job_config('task_1'): af.user_define_operation(processor=None) with af.job_config('task_2'): af.user_define_operation(processor=None) with af.job_config('task_3'): af.user_define_operation(processor=None) af.action_on_job_status(job_name='task_2', upstream_job_name='task_1') af.action_on_job_status(job_name='task_3', upstream_job_name='task_2', upstream_job_status=Status.RUNNING, action=JobAction.START) w = af.workflow_operation.submit_workflow( workflow_name='test_dag_generator') code = w.properties.get('code') self.assertTrue( "op_1.subscribe_event('test_dag_generator.task_1', 'TASK_STATUS_CHANGED', 'test_project', 'task_1')" in code) self.assertTrue( "op_2.subscribe_event('test_dag_generator.task_2', 'TASK_STATUS_CHANGED', 'test_project', 'task_2')" in code)
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)